def forward(self, input: Tensor, hx: Tensor) -> Tensor:
if hx is None:
hx = torch.zeros(input.size()[0], self.hidden_size, dtype=input.dtype, device=input.device)
rnncell = FSUMGUCell(self.input_size, self.hidden_size, bias=self.bias,
weight_ext_f=self.weight_f, bias_ext_f=self.bias_f, weight_ext_n=self.weight_n, bias_ext_n=self.bias_n,
hx_buffer=hx,
hwcfg=self.hwcfg, swcfg=self.swcfg).to(input.device)
iSource = BinGen(input, self.hwcfg, self.swcfg)().to(input.device)
iRNG = RNG(self.hwcfg, self.swcfg)().to(input.device)
iBSG = BSGen(iSource, iRNG, self.swcfg).to(input.device)
hSource = BinGen(hx, self.hwcfg, self.swcfg)().to(input.device)
hRNG = RNG(self.hwcfg, self.swcfg)().to(input.device)
hBSG = BSGen(hSource, hRNG, self.swcfg).to(input.device)
oPE = ProgError(torch.zeros(input.size()[0], self.hidden_size, dtype=input.dtype, device=input.device),
self.hwcfg_ope).to(input.device)
for c in range(2**self.hwcfg["width"]):
idx = torch.zeros(iSource.size(), dtype=torch.long, device=input.device)
iBS = iBSG(idx + c)
hdx = torch.zeros(hSource.size(), dtype=torch.long, device=input.device)
hBS = hBSG(hdx + c)
oBS = rnncell(iBS, hBS)
oPE.Monitor(oBS)
hy = oPE()[0]
return hy
def __init__(self,
hwcfg={
"entry": 4,
"rng": "Sobol",
"dimr": 4
},
swcfg={"stype": torch.float}):
super(CORDIV_kernel, self).__init__()
self.hwcfg = {}
self.hwcfg["entry"] = hwcfg["entry"]
self.hwcfg["rng"] = hwcfg["rng"].lower()
self.hwcfg["dimr"] = hwcfg["dimr"]
self.swcfg = {}
self.swcfg["stype"] = swcfg["stype"]
self.entry = hwcfg["entry"]
self.sr = ShiftReg(self.hwcfg, self.swcfg)
hwcfg_rng = {
"width": int(math.log2(self.entry)),
"dimr": hwcfg["dimr"],
"rng": hwcfg["rng"]
}
swcfg_rng = {"rtype": torch.long}
self.rng = RNG(hwcfg_rng, swcfg_rng)()
self.idx = torch.nn.Parameter(torch.zeros(1).type(torch.long),
requires_grad=False)
self.stype = swcfg["stype"]
self.historic_q = torch.nn.Parameter(torch.ones(1).type(self.stype),
requires_grad=False)
def test_bi2uni():
hwcfg = {
"width": 8,
"mode": "bipolar",
"dimr": 1,
"rng": "sobol",
"scale": 1,
"depth": 3
}
swcfg = {"rtype": torch.float, "stype": torch.float, "btype": torch.float}
bitwidth = hwcfg["width"]
mode = hwcfg["mode"]
rng = "Sobol"
in_dim = 1024
bitwidth = 8
in_mode = "bipolar"
out_mode = "unipolar"
stype = torch.float
btype = torch.float
rtype = torch.float
uBi2Uni = Bi2Uni(hwcfg, swcfg).to(device)
iVec = ((torch.rand(in_dim) * (2**bitwidth)).round() /
(2**bitwidth)).to(device)
start_time = time.time()
oVec = iVec.type(torch.float)
print("--- %s seconds ---" % (((time.time() - start_time)) * 2**bitwidth))
print("input", iVec)
print("real output", oVec)
hwcfg["mode"] = "bipolar"
iVecSource = BinGen(iVec, hwcfg, swcfg)().to(device)
iVecRNG = RNG(hwcfg, swcfg)().to(device)
iVecBS = BSGen(iVecSource, iVecRNG, swcfg).to(device)
iVecPE = ProgError(iVec, hwcfg).to(device)
hwcfg["mode"] = "unipolar"
oVecPE = ProgError(oVec, hwcfg).to(device)
hwcfg["mode"] = "bipolar"
with torch.no_grad():
idx = torch.zeros(iVecSource.size()).type(torch.long).to(device)
start_time = time.time()
for i in range((2**bitwidth)):
iBS = iVecBS(idx + i)
iVecPE.Monitor(iBS)
oVecU = uBi2Uni(iBS)
oVecPE.Monitor(oVecU)
print("--- %s seconds ---" % (time.time() - start_time))
print("final input error: ", min(iVecPE()[1]), max(iVecPE()[1]))
print("final output error:", min(oVecPE()[1]), max(oVecPE()[1]))
print("final output pp:", oVecPE()[0].data)
print("final output pe:", oVecPE()[1].data)
print("final output mean error:", oVecPE()[1].mean())
result_pe = oVecPE()[1].cpu().numpy()
# fig = plt.hist(result_pe, bins='auto') # arguments are passed to np.histogram
# plt.title("Histogram for final output error")
# plt.show()
print(result_pe)
print(result_pe.argmin(), result_pe.argmax())
print(result_pe[result_pe.argmin()], result_pe[result_pe.argmax()])
print(iVec[result_pe.argmin()], iVec[result_pe.argmax()])
def test_fsuconv2d():
plot_en = False
hwcfg_input = {"width": 8, "rng": "Sobol", "dimr": 1}
hwcfg = {
"width": 8,
"mode": "bipolar",
"scale": None,
"depth": 20,
"rng": "Sobol",
"dimr": 1
}
swcfg = {"btype": torch.float, "rtype": torch.float, "stype": torch.float}
rng = hwcfg["rng"]
in_channels = 32
out_channels = 16
kernel_size = 3
stride = 2
padding = 0
dilation = 1
groups = 1
bias = True
padding_mode = 'zeros'
modes = ["bipolar", "unipolar"]
scaled = [True, False]
result_pe = []
for mode in modes:
for scale in scaled:
hwcfg["mode"] = mode
hwcfg_input["mode"] = mode
hwcfg["scale"] = (kernel_size * kernel_size * in_channels +
bias) if scale else 1
length = 2**hwcfg["width"]
length_input = 2**hwcfg_input["width"]
result_pe_cycle = []
conv2d = torch.nn.Conv2d(in_channels,
out_channels,
kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
groups=groups,
bias=bias,
padding_mode=padding_mode).to(device)
if mode == "unipolar":
conv2d.weight.data = torch.rand(
out_channels, in_channels, kernel_size,
kernel_size).mul(length).round().div(length).to(device)
if bias is True:
conv2d.bias.data = torch.rand(out_channels).mul(
length).round().div(length).to(device)
elif mode == "bipolar":
conv2d.weight.data = torch.rand(
out_channels, in_channels, kernel_size, kernel_size).mul(
2).sub(1).mul(length).round().div(length).to(device)
if bias is True:
conv2d.bias.data = torch.rand(out_channels).mul(2).sub(
1).mul(length).round().div(length).to(device)
uconv2d = FSUConv2d(in_channels,
out_channels,
kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
groups=groups,
bias=bias,
padding_mode=padding_mode,
weight_ext=conv2d.weight,
bias_ext=conv2d.bias,
hwcfg=hwcfg,
swcfg=swcfg).to(device)
input_size = (128, 32)
iVec = (
(torch.rand(32, in_channels, input_size[0], input_size[1]) *
length_input).round() / length_input).to(device)
oVec = conv2d(iVec)
iVecSource = BinGen(iVec, hwcfg, swcfg)().to(device)
iVecRNG = RNG(hwcfg_input, swcfg)().to(device)
iVecBS = BSGen(iVecSource, iVecRNG, swcfg).to(device)
hwcfg["scale"] = 1
iVecPE = ProgError(iVec, hwcfg).to(device)
hwcfg["scale"] = (kernel_size * kernel_size * in_channels +
bias) if scale else 1
oVecPE = ProgError(oVec, hwcfg).to(device)
with torch.no_grad():
idx = torch.zeros(iVecSource.size()).type(
torch.long).to(device)
start_time = time.time()
for i in range(length):
iBS = iVecBS(idx + i)
iVecPE.Monitor(iBS)
oVecU = uconv2d(iBS)
oVecPE.Monitor(oVecU)
rmse = torch.sqrt(
torch.sum(torch.mul(oVecPE()[1],
oVecPE()[1])) /
torch.prod(torch.tensor(oVecPE()[1].size())))
if plot_en is True:
result_pe_cycle.append(1 - rmse.item())
print("--- %s seconds ---" % (time.time() - start_time))
print("RNG: " + rng + ", data: " + mode + ", scaled: " +
str(scale))
print("input error: ", "min: ",
torch.min(iVecPE()[1]).item(), "max: ",
torch.max(iVecPE()[1]).item())
print("output error: ", "min: ",
torch.min(oVecPE()[1]).item(), "max: ",
torch.max(oVecPE()[1]).item(), "RMSE: ", rmse.item())
print()
if plot_en is True:
result_pe = oVecPE()[1].cpu().numpy()
print("error distribution=========>")
plt.figure(figsize=(3, 1.5))
fig = plt.hist(
result_pe.flatten(),
bins='auto') # arguments are passed to np.histogram
plt.show()
print("progressive accuracy=========>")
plt.figure(figsize=(3, 1.5))
fig = plt.plot(result_pe_cycle
) # arguments are passed to np.histogram
plt.show()
def test_fsudiv():
hwcfg = {
"width": 8,
"mode": "unipolar",
"rng": "Sobol",
"dimr": 4,
"scale": 1,
"depth_sa": 3,
"depth_ss": 2,
"entry_kn": 2
}
swcfg = {"rtype": torch.float, "stype": torch.float, "btype": torch.float}
bitwidth = hwcfg["width"]
mode = hwcfg["mode"]
total_cnt = 1
savepdf = False
stype = swcfg["stype"]
btype = swcfg["btype"]
rtype = swcfg["rtype"]
print("========================================================")
print(mode)
print("========================================================")
if mode == "unipolar":
# all values in unipolar are non-negative
# dividend is always non greater than divisor
# divisor is non-zero
low_bound = 0
up_bound = 2**bitwidth
elif mode == "bipolar":
# values in bipolar are arbitrarily positive or negative
# abs of dividend is always non greater than abs of divisor
# abs of divisor is non-zero
low_bound = -2**(bitwidth - 1)
up_bound = 2**(bitwidth - 1)
divisor_list = []
dividend_list = []
for divisor_val in range(up_bound, low_bound - 1, -1):
divisor_list.append([])
dividend_list.append([])
for dividend_val in range(low_bound, up_bound + 1, 1):
divisor_list[up_bound - divisor_val].append(divisor_val)
dividend_list[up_bound - divisor_val].append(dividend_val)
dividend = torch.tensor(dividend_list).type(
torch.float).div(up_bound).to(device)
divisor = torch.tensor(divisor_list).type(
torch.float).div(up_bound).to(device)
quotient = dividend.div(divisor)
# find the invalid postions in quotient
quotient_nan = torch.isnan(quotient)
quotient_inf = torch.isinf(quotient)
quotient_mask = quotient_nan + quotient_inf
quotient[quotient_mask] = 0
quotient = quotient.clamp(-1, 1)
result_pe_total = []
for rand_idx in range(1, total_cnt + 1):
quotientPE = ProgError(quotient, hwcfg).to(device)
dividendPE = ProgError(dividend, hwcfg).to(device)
dividendSRC = BinGen(dividend, hwcfg, swcfg)().to(device)
divisorPE = ProgError(divisor, hwcfg).to(device)
divisorSRC = BinGen(divisor, hwcfg, swcfg)().to(device)
dut_div = FSUDiv(hwcfg, swcfg).to(device)
hwcfg["dimr"] = 1
dividendRNG = RNG(hwcfg, swcfg)().to(device)
dividendBS = BSGen(dividendSRC, dividendRNG, swcfg).to(device)
divisorRNG = RNG(hwcfg, swcfg)().to(device)
divisorBS = BSGen(divisorSRC, divisorRNG, swcfg).to(device)
with torch.no_grad():
start_time = time.time()
for i in range(2**bitwidth):
dividend_bs = dividendBS(torch.tensor([i]))
dividendPE.Monitor(dividend_bs)
divisor_bs = divisorBS(torch.tensor([i]))
divisorPE.Monitor(divisor_bs)
quotient_bs = dut_div(dividend_bs, divisor_bs)
quotientPE.Monitor(quotient_bs)
# get the result for different rng
result_pe = quotientPE()[1].cpu().numpy()
result_pe[quotient_mask.cpu().numpy()] = np.nan
result_pe_total.append(result_pe)
# get the result for different rng
result_pe_total = np.array(result_pe_total)
#######################################################################
# check the error of all simulation
#######################################################################
result_pe_total_no_nan = result_pe_total[~np.isnan(result_pe_total)]
print("RMSE:{:1.4}".format(math.sqrt(np.mean(result_pe_total_no_nan**2))))
print("MAE: {:1.4}".format(np.mean(np.abs(result_pe_total_no_nan))))
print("bias:{:1.4}".format(np.mean(result_pe_total_no_nan)))
print("max: {:1.4}".format(np.max(result_pe_total_no_nan)))
print("min: {:1.4}".format(np.min(result_pe_total_no_nan)))
def test_fsumul():
hwcfg = {
"width": 8,
"mode": "bipolar",
"dimr": 1,
"dima": 0,
"rng": "sobol",
"scale": 1,
"depth": 10,
"entry": None,
"static": True
}
swcfg = {"rtype": torch.float, "stype": torch.float, "btype": torch.float}
bitwidth = hwcfg["width"]
col = 100
modes = ["bipolar", "unipolar"]
for mode in modes:
if mode == "unipolar":
input_prob = torch.rand(col).mul(2**bitwidth).round().div(
2**bitwidth).to(device)
iVec = torch.rand(col).mul(2**bitwidth).round().div(
2**bitwidth).to(device)
elif mode == "bipolar":
input_prob = torch.rand(col).mul(2).sub(1).mul(
2**bitwidth).round().div(2**bitwidth).to(device)
iVec = torch.rand(col).mul(2).sub(1).mul(2**bitwidth).round().div(
2**bitwidth).to(device)
hwcfg["mode"] = mode
dut_mul = FSUMul(input_prob, hwcfg, swcfg).to(device)
oVec = torch.mul(iVec, input_prob).mul(2**bitwidth).round().div(
2**bitwidth).to(device)
iVecPE = ProgError(iVec, hwcfg).to(device)
oVecPE = ProgError(oVec, hwcfg).to(device)
iVecSource = BinGen(iVec, hwcfg, swcfg)().to(device)
iVecRNG = RNG(hwcfg, swcfg)().to(device)
iVecBS = BSGen(iVecSource, iVecRNG, swcfg).to(device)
with torch.no_grad():
start_time = time.time()
for i in range(
2**
bitwidth): # unary cycle count 2^n for n-bit binary data
iBS = iVecBS(torch.tensor([i])) # input bit stream generation
iVecPE.Monitor(iBS) # input accuracy measurement
oVecU = dut_mul(iBS) # computing kernel, e.g., multiplication
oVecPE.Monitor(oVecU) # output accuracy measurement
print("--- %s seconds ---" % (time.time() - start_time))
print("input error: ", torch.min(iVecPE()[1]),
torch.max(iVecPE()[1]))
print("output error: ", torch.min(oVecPE()[1]),
torch.max(oVecPE()[1]))
result_pe = oVecPE()[1].cpu().numpy()
print("RMSE", math.sqrt(sum(result_pe**2) / len(result_pe)))
print("bias", sum(result_pe) / len(result_pe))
def test_fsusqrt(mode="unipolar",
bitwidth=8,
emit=True,
jk_trace=False,
depth_kernel=1,
depth_sr=4,
savepdf=False,
total_cnt=1):
hwcfg = {
"width": bitwidth,
"mode": mode,
"dima": 0,
"scale": 1,
"depth": 10,
"entry": None,
"jk_trace": jk_trace,
"emit": emit,
"entry_kn": depth_kernel,
"entry_sr": depth_sr,
"rng": "Sobol",
"dimr": 4
}
swcfg = {"rtype": torch.float, "stype": torch.float, "btype": torch.float}
bitwidth = hwcfg["width"]
rng = hwcfg["rng"]
print("========================================================")
print(mode)
print("========================================================")
# all input values are non-negative
low_bound = 0
if mode == "unipolar":
up_bound = 2**bitwidth
elif mode == "bipolar":
low_bound = 0
up_bound = 2**(bitwidth - 1)
input_list = []
for input_val in range(low_bound, up_bound + 1, 1):
input_list.append(input_val)
input = torch.tensor(input_list).type(torch.float).div(up_bound).to(device)
output = torch.sqrt(input).to(device)
result_pe_total = []
for rand_idx in range(1, total_cnt + 1):
outputPE = ProgError(output, hwcfg).to(device)
inputPE = ProgError(input, hwcfg).to(device)
inputSRC = BinGen(input, hwcfg, swcfg)().to(device)
dut_sqrt = FSUSqrt(hwcfg, swcfg).to(device)
inputRNG = RNG(hwcfg, swcfg)().to(device)
inputBS = BSGen(inputSRC, inputRNG, swcfg).to(device)
with torch.no_grad():
start_time = time.time()
for i in range(2**bitwidth):
input_bs = inputBS(torch.tensor([i]))
inputPE.Monitor(input_bs)
ouyput_bs = dut_sqrt(input_bs)
outputPE.Monitor(ouyput_bs)
# get the result for different rng
result_pe = outputPE()[1].cpu().numpy()
result_pe_total.append(result_pe)
# get the result for different rng
result_pe_total = np.array(result_pe_total)
#######################################################################
# check the error of all simulation
#######################################################################
print("RMSE:{:1.4}".format(math.sqrt(np.mean(result_pe_total**2))))
print("MAE: {:1.4}".format(np.mean(np.abs(result_pe_total))))
print("bias:{:1.4}".format(np.mean(result_pe_total)))
print("max: {:1.4}".format(np.max(result_pe_total)))
print("min: {:1.4}".format(np.min(result_pe_total)))
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
bias=True,
padding_mode='zeros',
weight_ext=None,
bias_ext=None,
hwcfg={
"widthi": 8,
"rngi": "Sobol",
"quantilei": 1,
"widthw": 8,
"rngw": "Sobol",
"quantilew": 1,
"cycle": 128,
"rounding": "round",
"signmag": True
}):
super(HUBConv2d,
self).__init__(in_channels, out_channels, kernel_size, stride,
padding, dilation, groups, bias, padding_mode)
self.hwcfg = {}
self.hwcfg["widthi"] = hwcfg["widthi"]
self.hwcfg["rngi"] = hwcfg["rngi"].lower()
self.hwcfg["quantilei"] = hwcfg["quantilei"]
self.hwcfg["widthw"] = hwcfg["widthw"]
self.hwcfg["rngw"] = hwcfg["rngw"].lower()
self.hwcfg["quantilew"] = hwcfg["quantilew"]
self.hwcfg["rounding"] = hwcfg["rounding"].lower()
self.hwcfg["signmag"] = hwcfg["signmag"]
self.hwcfg["cycle"] = min(
hwcfg["cycle"],
2**(max(hwcfg["widthi"], hwcfg["widthw"]) - hwcfg["signmag"]))
self.itc = (self.hwcfg["rngi"] in ["race", "tc", "race10", "tc10"])
self.wtc = (self.hwcfg["rngw"] in ["race", "tc", "race10", "tc10"])
assert not (self.itc and self.wtc), \
"Error: the hw config 'rngi' and 'rngw' in " + str(self) + " class can't adopt temporal coding simultaneously."
assert self.hwcfg["quantilei"] > 0 and self.hwcfg["quantilei"] <= 1, \
"Error: the hw config 'quantilei' of " + str(self) + " class needs to be within (0, 1]."
assert self.hwcfg["quantilew"] > 0 and self.hwcfg["quantilew"] <= 1, \
"Error: the hw config 'quantilew' of " + str(self) + " class needs to be within (0, 1]."
assert self.hwcfg["rounding"] in ["round", "ceil", "floor"], \
"Error: the hw config 'rounding' of " + str(self) + " class requires one of ['round', 'ceil', 'floor']."
assert self.hwcfg["signmag"] is True, \
"Error: the hw config 'signmag' of " + str(self) + " class requires to be True, i.e., always computing on sign-magnitue data, for diverse architectures."
# maximum possible run cycle
self.cycle_max = 2**(max(hwcfg["widthi"], hwcfg["widthw"]) -
hwcfg["signmag"])
# actual run cycle
self.cycle_act = min(
hwcfg["cycle"],
2**(max(hwcfg["widthi"], hwcfg["widthw"]) - hwcfg["signmag"]))
assert groups == 1, \
"Error: the 'groups' in " + str(self) + " class requires to be 1."
assert padding_mode == 'zeros', \
"Error: the 'padding_mode' in " + str(self) + " class requires to be 'zeros'."
# weight and bias
if weight_ext is not None:
assert (weight_ext.size()[0], weight_ext.size()[1], weight_ext.size()[2], weight_ext.size()[3]) == (out_channels, in_channels, num2tuple(kernel_size)[0], num2tuple(kernel_size)[1]), \
"Error: the hw config 'out_channels, in_channels, kernel_size' in " + str(self) + " class unmatches the binary weight shape."
self.weight.data = weight_ext
if bias and (bias_ext is not None):
assert bias_ext.size()[0] == out_channels, \
"Error: the hw config 'out_channels' in " + str(self) + " class unmatches the binary bias shape."
self.bias.data = bias_ext
swcfg = {
"btype": torch.float,
"rtype": torch.float,
"stype": torch.float
}
# random_sequence from RNG
hwcfg_irng = {
"width": self.hwcfg["widthi"] - self.hwcfg["signmag"],
"dimr": 1,
"rng": self.hwcfg["rngi"]
}
self.irng = RNG(hwcfg_irng, swcfg)()
hwcfg_wrng = {
"width": self.hwcfg["widthw"] - self.hwcfg["signmag"],
"dimr": 1,
"rng": self.hwcfg["rngw"]
}
self.wrng = RNG(hwcfg_wrng, swcfg)()
if (self.itc) and (not self.wtc):
# cbsg controller is input
self.rngctler = self.irng
self.rngctlee = self.wrng
elif (not self.itc) and (self.wtc):
# cbsg controller is weight
self.rngctler = self.wrng
self.rngctlee = self.irng
elif (not self.itc) and (not self.wtc):
# when rate coding is applied to both input and weight, always control weight with input
# the hardware cost of doing this is similar to the opposite
self.rngctler = self.irng
self.rngctlee = self.wrng
# generate the value map for mul using current rng
# dim 0 is input index
# the tensor input value is the actual value produced by the rngctler
self.mapctler = torch.nn.Parameter(torch.empty(self.cycle_max),
requires_grad=False)
cycle_ctlerval = torch.empty(0)
torch.cat(
self.cycle_max *
[torch.arange(self.cycle_max, dtype=torch.float).unsqueeze(1)],
1,
out=cycle_ctlerval)
cycle_ctlerbit = torch.empty(0)
torch.gt(cycle_ctlerval,
self.rngctler.unsqueeze(0),
out=cycle_ctlerbit)
self.mapctler.data = torch.sum(cycle_ctlerbit,
1).squeeze_().type(torch.long)
# dim 0 is input index, dim 1 is weight index
# the tensor value is the actual weight value produced by the rngctlee, under a specific input and weight
self.mapctlee = torch.nn.Parameter(torch.empty(self.cycle_max,
self.cycle_max),
requires_grad=False)
cycle_ctleebit = torch.empty(0)
torch.gt(cycle_ctlerval,
self.rngctlee.unsqueeze(0),
out=cycle_ctleebit)
for c in range(self.cycle_max):
self.mapctlee.data[c] = torch.sum(
cycle_ctleebit[:, 0:self.mapctler.data[c]], 1).squeeze_()
self.rshift_i = None
self.rshift_w = None
self.rshift_o = None
def __init__(
self,
hwcfg={
"mode": "bipolar",
"jk_trace": True,
"emit": True,
"entry_kn": 1,
"entry_sr": 2,
"rng": "Sobol",
"dimr": 4
},
swcfg={
"stype": torch.float,
"btype": torch.float
}):
super(FSUSqrt, self).__init__()
self.hwcfg = {}
self.hwcfg["mode"] = hwcfg["mode"].lower()
self.hwcfg["jk_trace"] = hwcfg["jk_trace"]
self.hwcfg["emit"] = hwcfg["emit"]
self.hwcfg["entry_kn"] = hwcfg["entry_kn"]
self.hwcfg["entry_sr"] = hwcfg["entry_sr"]
self.hwcfg["rng"] = hwcfg["rng"].lower()
self.hwcfg["dimr"] = hwcfg["dimr"]
self.swcfg = {}
self.swcfg["btype"] = swcfg["btype"]
self.swcfg["stype"] = swcfg["stype"]
# data representation
self.mode = hwcfg["mode"].lower()
assert self.mode in ["unipolar", "bipolar"], \
"Error: the hw config 'mode' in " + str(self) + " class requires one of ['unipolar', 'bipolar']."
self.jk_trace = hwcfg["jk_trace"]
self.emit = hwcfg["emit"]
self.entry_kn = hwcfg["entry_kn"]
self.entry_sr = hwcfg["entry_sr"]
assert math.ceil(math.log2(self.entry_kn)) == math.floor(math.log2(self.entry_kn)) , \
"Eroor: the hw config 'entry_kn' in " + str(self) + " class needs to be power of 2."
assert math.ceil(math.log2(self.entry_sr)) == math.floor(math.log2(self.entry_sr)) , \
"Eroor: the hw config 'entry_sr' in " + str(self) + " class needs to be power of 2."
self.stype = swcfg["stype"]
if self.emit is True:
self.emit_out = torch.nn.Parameter(torch.zeros(1).type(torch.int8),
requires_grad=False)
hwcfg_add = {
"mode": "unipolar",
"scale": 1,
"dima": 0,
"depth": 10,
"entry": None,
}
swcfg_add = {
"btype": swcfg["btype"],
"stype": torch.int8,
}
self.nsadd = FSUAdd(hwcfg_add, swcfg_add)
hwcfg_sr = {"entry": self.entry_sr}
swcfg_sr = {
"btype": swcfg["btype"],
"stype": torch.int8,
}
self.sr = ShiftReg(hwcfg_sr, swcfg_sr)
hwcfg_rng = {
"width": int(math.log2(self.entry_sr)),
"dimr": 1,
"rng": hwcfg["rng"]
}
swcfg_rng = {"rtype": torch.long}
self.rng = RNG(hwcfg_rng, swcfg_rng)()
self.idx = torch.nn.Parameter(torch.zeros(1).type(torch.long),
requires_grad=False)
if self.mode == "bipolar":
hwcfg_b2u = {"depth": 3}
swcfg_b2u = {"btype": swcfg["btype"], "stype": torch.int8}
self.bi2uni_emit = Bi2Uni(hwcfg_b2u, swcfg_b2u)
else:
self.trace = torch.nn.Parameter(torch.zeros(1).type(torch.int8),
requires_grad=False)
if self.mode == "bipolar":
hwcfg_b2u = {"depth": 3}
swcfg_b2u = {"btype": swcfg["btype"], "stype": torch.int8}
self.bi2uni = Bi2Uni(hwcfg_b2u, swcfg_b2u)
if self.jk_trace is True:
swcfg_jkff = {"stype": torch.int8}
self.jkff = JKFF(swcfg_jkff)
else:
hwcfg_cordiv_kernel = {
"entry": 4,
"rng": hwcfg["rng"],
"dimr": 4
}
swcfg_cordiv_kernel = {"stype": torch.int8}
self.cordiv_kernel = CORDIV_kernel(hwcfg_cordiv_kernel,
swcfg_cordiv_kernel)
self.dff = torch.nn.Parameter(torch.zeros(1).type(torch.int8),
requires_grad=False)
def __init__(self,
in_1_prob=None,
hwcfg={
"width": 8,
"mode": "bipolar",
"static": False,
"rng": "Sobol",
"dimr": 1
},
swcfg={
"rtype": torch.float,
"stype": torch.float
}):
super(FSUMul, self).__init__()
self.hwcfg = {}
self.hwcfg["width"] = hwcfg["width"]
self.hwcfg["mode"] = hwcfg["mode"].lower()
self.hwcfg["static"] = hwcfg["static"]
self.hwcfg["rng"] = hwcfg["rng"].lower()
self.hwcfg["dimr"] = hwcfg["dimr"]
self.swcfg = {}
self.swcfg["rtype"] = swcfg["rtype"]
self.swcfg["stype"] = swcfg["stype"]
self.entry = 2**hwcfg["width"]
self.static = hwcfg["static"]
self.stype = swcfg["stype"]
self.rtype = swcfg["rtype"]
self.mode = hwcfg["mode"].lower()
assert self.mode in ["unipolar", "bipolar"], \
"Error: the hw config 'mode' in " + str(self) + " class requires one of ['unipolar', 'bipolar']."
# the random number generator used in computation
self.rng = RNG(hwcfg, swcfg)()
if self.static is True:
# the probability of in_1 used in static computation
self.in_1_prob = in_1_prob
assert in_1_prob is not None, \
"Error: the static multiplier requires in_1_prob in " + str(self) + " class."
# directly create an unchange bitstream generator for static computation
self.source_gen = BinGen(self.in_1_prob, hwcfg, swcfg)()
self.bsg = BSGen(self.source_gen, self.rng, {"stype": torch.int8})
# rng_idx is used later as an enable signal, get update every cycled
self.rng_idx = torch.nn.Parameter(torch.zeros(1).type(torch.long),
requires_grad=False)
# Generate two seperate bitstream generators and two enable signals for bipolar mode
if self.mode == "bipolar":
self.bsg_inv = BSGen(self.source_gen, self.rng,
{"stype": torch.int8})
self.rng_idx_inv = torch.nn.Parameter(torch.zeros(1).type(
torch.long),
requires_grad=False)
else:
# use a shift register to store the count of 1s in one bitstream to generate data
sr_hwcfg = {"entry": self.entry}
self.sr = ShiftReg(sr_hwcfg, swcfg)
self.rng_idx = torch.nn.Parameter(torch.zeros(1).type(torch.long),
requires_grad=False)
if self.mode == "bipolar":
self.rng_idx_inv = torch.nn.Parameter(torch.zeros(1).type(
torch.long),
requires_grad=False)
def test_rng():
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
hwcfg = {
"width" : 4,
"dimr" : 1,
"rng" : "sobol"
}
swcfg={
"rtype" : torch.float
}
rng = RNG(hwcfg, swcfg)()
print(hwcfg["rng"], rng.to(device))
hwcfg["rng"] = "rc"
rng = RNG(hwcfg, swcfg)()
print(hwcfg["rng"], rng.to(device))
hwcfg["rng"] = "race"
rng = RNG(hwcfg, swcfg)()
print(hwcfg["rng"], rng.to(device))
hwcfg["rng"] = "tc"
rng = RNG(hwcfg, swcfg)()
print(hwcfg["rng"], rng.to(device))
hwcfg["rng"] = "lfsr"
rng = RNG(hwcfg, swcfg)()
print(hwcfg["rng"], rng.to(device))
hwcfg["rng"] = "sys"
rng = RNG(hwcfg, swcfg)()
print(hwcfg["rng"], rng.to(device))
hwcfg["rng"] = "race10"
rng = RNG(hwcfg, swcfg)()
print(hwcfg["rng"], rng.to(device))
hwcfg["rng"] = "tc10"
rng = RNG(hwcfg, swcfg)()
print(hwcfg["rng"], rng.to(device))
def test_fsumul_in_stream():
bitwidth = 12
depth = 4
hwcfg = {
"width": bitwidth,
"mode": "bipolar",
"dimr": 1,
"dima": 0,
"rng": "sobol",
"scale": 1,
"depth": 10,
"entry": None,
"static": False
}
swcfg = {"rtype": torch.float, "stype": torch.float, "btype": torch.float}
col = 100
modes = ["bipolar", "unipolar"]
for mode in modes:
if mode == "unipolar":
input_prob_0 = torch.rand(col).mul(2**bitwidth).round().div(
2**bitwidth).to(device)
input_prob_1 = torch.rand(col).mul(2**bitwidth).round().div(
2**bitwidth).to(device)
elif mode == "bipolar":
input_prob_0 = torch.rand(col).mul(2).sub(1).mul(
2**bitwidth).round().div(2**bitwidth).to(device)
input_prob_1 = torch.rand(col).mul(2).sub(1).mul(
2**bitwidth).round().div(2**bitwidth).to(device)
hwcfg["mode"] = mode
hwcfg["width"] = depth
dut_mul = FSUMul(None, hwcfg, swcfg).to(device)
hwcfg["width"] = bitwidth
oVec = torch.mul(input_prob_0, input_prob_1).mul(
2**bitwidth).round().div(2**bitwidth).to(device)
prob_0_PE = ProgError(input_prob_0, hwcfg).to(device)
prob_1_PE = ProgError(input_prob_1, hwcfg).to(device)
oVecPE = ProgError(oVec, hwcfg).to(device)
prob_0_Source = BinGen(input_prob_0, hwcfg, swcfg)().to(device)
prob_1_Source = BinGen(input_prob_1, hwcfg, swcfg)().to(device)
iVecRNG0 = RNG(hwcfg, swcfg)().to(device)
iVecRNG1 = RNG(hwcfg, swcfg)().to(device)
prob_0_BS = BSGen(prob_0_Source, iVecRNG0, swcfg).to(device)
prob_1_BS = BSGen(prob_1_Source, iVecRNG1, swcfg).to(device)
with torch.no_grad():
start_time = time.time()
idx = torch.zeros(input_prob_0.size()).type(torch.long).to(device)
for i in range(2**bitwidth):
#print(i)
iBS_0 = prob_0_BS(idx + i)
iBS_1 = prob_1_BS(idx + i)
prob_0_PE.Monitor(iBS_0)
prob_1_PE.Monitor(iBS_1)
oVecU = dut_mul(iBS_0, iBS_1)
oVecPE.Monitor(oVecU)
print("--- %s seconds ---" % (time.time() - start_time))
print(mode)
print("input 0 error: ", "min:", torch.min(prob_0_PE()[1]), "max:",
torch.max(prob_0_PE()[1]))
print("input 1 error: ", "min:", torch.min(prob_1_PE()[1]), "max:",
torch.max(prob_1_PE()[1]))
print("output error: ", "min:", torch.min(oVecPE()[1]), "max:",
torch.max(oVecPE()[1]), "rmse:",
torch.sqrt(torch.mean(torch.mul(oVecPE()[1],
oVecPE()[1]))), "bias:",
torch.mean(oVecPE()[1]))
def test_fsusignabs():
total_cnt = 5
hwcfg = {
"width": 8,
"mode": "bipolar",
"dimr": 1,
"rng": "sobol",
"scale": 1,
"depth": 5
}
swcfg = {"rtype": torch.float, "stype": torch.float, "btype": torch.float}
bitwidth = hwcfg["width"]
mode = hwcfg["mode"]
print("========================================================")
print(mode)
print("========================================================")
# all input values are non-negative
low_bound = 0
if mode == "unipolar":
up_bound = 2**bitwidth
elif mode == "bipolar":
low_bound = -2**(bitwidth - 1)
up_bound = 2**(bitwidth - 1)
input_list = []
for input_val in range(low_bound, up_bound + 1, 1):
input_list.append(input_val)
input = torch.tensor(input_list).type(torch.float).div(up_bound).to(device)
# input = torch.tensor([-1/256]).type(torch.float).div(up_bound).to(device)
output = torch.abs(input).to(device)
result_pe_total = []
for rand_idx in range(1, total_cnt + 1):
outputPE = ProgError(output, hwcfg).to(device)
inputPE = ProgError(input, hwcfg).to(device)
inputSRC = BinGen(input, hwcfg, swcfg)().to(device)
dut = FSUSignAbs(hwcfg, swcfg).to(device)
inputRNG = RNG(hwcfg, swcfg)().to(device)
inputBS = BSGen(inputSRC, inputRNG, swcfg).to(device)
with torch.no_grad():
start_time = time.time()
for i in range(2**bitwidth):
input_bs = inputBS(torch.tensor([i]))
inputPE.Monitor(input_bs)
_, output_bs = dut(input_bs)
outputPE.Monitor(output_bs)
# get the result for different rng
result_pe = outputPE()[1].cpu().numpy()
result_pe_total.append(result_pe)
# get the result for different rng
result_pe_total = np.array(result_pe_total)
#######################################################################
# check the error of all simulation
#######################################################################
print("RMSE:{:1.4}".format(np.sqrt(np.mean(result_pe_total**2))))
print("MAE: {:1.4}".format(np.mean(np.abs(result_pe_total))))
print("bias:{:1.4}".format(np.mean(result_pe_total)))
print("max: {:1.4}".format(np.max(result_pe_total)))
print("min: {:1.4}".format(np.min(result_pe_total)))
def __init__(self,
in_features,
out_features,
bias=True,
weight_ext=None,
bias_ext=None,
hwcfg={
"width": 8,
"mode": "bipolar",
"rng": "Sobol",
"dimr": 1
},
swcfg={
"btype": torch.float,
"rtype": torch.float,
"stype": torch.float
}):
super(FSULinearPC, self).__init__(in_features, out_features, bias=bias)
self.hwcfg = {}
self.hwcfg["width"] = hwcfg["width"]
self.hwcfg["mode"] = hwcfg["mode"].lower()
self.hwcfg["rng"] = hwcfg["rng"].lower()
self.hwcfg["dimr"] = hwcfg["dimr"]
self.swcfg = {}
self.swcfg["btype"] = swcfg["btype"]
self.swcfg["rtype"] = swcfg["rtype"]
self.swcfg["stype"] = swcfg["stype"]
self.mode = hwcfg["mode"].lower()
assert self.mode in ["unipolar", "bipolar"], \
"Error: the hw config 'mode' in " + str(self) + " class requires one of ['unipolar', 'bipolar']."
# bias indication for original linear layer
self.has_bias = bias
# RNG for weight
hwcfg_wrng = {
"width": hwcfg["width"],
"rng": hwcfg["rng"],
"dimr": hwcfg["dimr"]
}
self.wrng = RNG(hwcfg_wrng, swcfg)()
if hwcfg["rng"].lower() in ["race", "tc", "race10", "tc10"]:
self.wtc = True
else:
self.wtc = False
# define the linear weight and bias
if weight_ext is not None:
assert (weight_ext.size()[0], weight_ext.size()[1]) == (out_features, in_features), \
"Error: the hw config 'out_features, in_features' in " + str(self) + " class unmatches the binary weight shape."
self.weight.data = BinGen(weight_ext, self.hwcfg, self.swcfg)()
if bias and (bias_ext is not None):
assert bias_ext.size()[0] == out_features, \
"Error: the hw config 'out_features' in " + str(self) + " class unmatches the binary bias shape."
self.bias.data = BinGen(bias_ext, self.hwcfg, self.swcfg)()
# RNG for bias, same as RNG for weight
hwcfg_brng = {
"width": hwcfg["width"],
"rng": hwcfg["rng"],
"dimr": hwcfg["dimr"]
}
self.brng = RNG(hwcfg_brng, swcfg)()
# define the kernel linear for input bit 1
self.wbsg_i1 = BSGen(self.weight, self.wrng, swcfg)
self.wrdx_i1 = torch.nn.Parameter(torch.zeros_like(self.weight,
dtype=torch.long),
requires_grad=False).unsqueeze(0)
if self.has_bias is True:
self.bbsg = BSGen(self.bias, self.brng, swcfg)
self.brdx = torch.nn.Parameter(torch.zeros_like(self.bias,
dtype=torch.long),
requires_grad=False)
# if bipolar, define a kernel for input bit 0, note that there is no bias required for this kernel
if (self.mode == "bipolar") and (self.wtc is False):
self.wbsg_i0 = BSGen(self.weight, self.wrng, swcfg)
self.wrdx_i0 = torch.nn.Parameter(torch.zeros_like(
self.weight, dtype=torch.long),
requires_grad=False).unsqueeze(0)
def test_fsuadd():
hwcfg = {
"width": 12,
"mode": "bipolar",
"dimr": 1,
"dima": 0,
"rng": "sobol",
"scale": 1,
"depth": 20,
"entry": None
}
swcfg = {"rtype": torch.float, "stype": torch.float, "btype": torch.float}
bitwidth = hwcfg["width"]
rng = hwcfg["rng"]
plot_en = False
modes = ["bipolar", "unipolar"]
size = [128, 256, 512]
scaled = [True, False]
result_pe = []
for mode in modes:
for scale in scaled:
run_time = 0
acc_dim = hwcfg["dima"]
scale_mod = size[acc_dim]
result_pe_cycle = []
hwcfg["mode"] = mode
hwcfg["scale"] = scale_mod if scale else 1
uadd = FSUAdd(hwcfg, swcfg).to(device)
if mode == "unipolar":
iVec = torch.rand(size).mul(2**bitwidth).round().div(
2**bitwidth).to(device)
elif mode == "bipolar":
iVec = torch.rand(size).mul(2).sub(1).mul(
2**bitwidth).round().div(2**bitwidth).to(device)
oVec = torch.sum(iVec, acc_dim).to(device)
iVecSource = BinGen(iVec, hwcfg, swcfg)().to(device)
iVecRNG = RNG(hwcfg, swcfg)().to(device)
iVecBS = BSGen(iVecSource, iVecRNG, swcfg).to(device)
hwcfg["scale"] = 1
iVecPE = ProgError(iVec, hwcfg).to(device)
print("iVecPE cfg", iVecPE.hwcfg)
hwcfg["scale"] = scale_mod if scale else 1
oVecPE = ProgError(oVec, hwcfg).to(device)
print("oVecPE cfg", oVecPE.hwcfg)
with torch.no_grad():
idx = torch.zeros(iVecSource.size()).type(
torch.long).to(device)
for i in range(2**bitwidth):
iBS = iVecBS(idx + i)
iVecPE.Monitor(iBS)
start_time = time.time()
oVecU = uadd(iBS)
run_time = time.time() - start_time + run_time
if i == 0:
print("uadd cfg", uadd.hwcfg)
oVecPE.Monitor(oVecU)
rmse = torch.sqrt(
torch.mean(torch.mul(oVecPE()[1],
oVecPE()[1])))
result_pe_cycle.append(1 - rmse.item())
print("--- %s seconds ---" % (time.time() - start_time))
print("RNG: " + rng + ", data: " + mode + ", scaled: " +
str(scale))
print("input error: ", "min: ",
torch.min(iVecPE()[1]).item(), "max: ",
torch.max(iVecPE()[1]).item())
print("output error: ", "min: ",
torch.min(oVecPE()[1]).item(), "max: ",
torch.max(oVecPE()[1]).item(), "RMSE: ", rmse.item())
print()
if plot_en is True:
result_pe = oVecPE()[1].cpu().numpy()
print("error distribution=========>")
plt.figure(figsize=(3, 1.5))
fig = plt.hist(
result_pe.flatten(),
bins='auto') # arguments are passed to np.histogram
plt.show()
print("progressive accuracy=========>")
plt.figure(figsize=(3, 1.5))
fig = plt.plot(result_pe_cycle
) # arguments are passed to np.histogram
plt.show()
def test_fsumgu():
bitwidth_list = [7, 8, 9, 10]
for bitwidth in bitwidth_list:
print("bit width:", bitwidth)
win_sz = 10
batch = 32
input_sz = 256
hidden_sz = 64
intwidth = 1
fracwidth = bitwidth - intwidth
mode = "bipolar"
depth = bitwidth + 2
depth_ismul = bitwidth - 4
rng = "Sobol"
bias = False
output_error_only = True
hwcfg = {
"width": bitwidth,
"mode": mode,
"depth": depth,
"depth_ismul": depth_ismul,
"rng": rng,
"dimr": 1,
"scale": 1
}
swcfg = {
"btype": torch.float,
"rtype": torch.float,
"stype": torch.float
}
input = torch.randn(win_sz, batch, input_sz).to(device)
input = truncated_normal(input, mean=0, std=0.4)
hx1 = torch.randn(batch, hidden_sz).to(device)
hx1 = truncated_normal(hx1, mean=0, std=0.1)
hx2 = hx1.clone().detach().to(device)
hx3 = hx1.clone().detach().to(device)
hx4 = hx1.clone().detach().to(device)
output1 = []
output2 = []
output3 = []
output4 = []
rnn1 = HardMGUCell(input_sz, hidden_sz, bias=bias,
hard=True).to(device)
rnn3 = HardMGUCellFXP(input_sz,
hidden_sz,
bias=bias,
hard=True,
intwidth=intwidth,
fracwidth=fracwidth).to(device)
rnn3.weight_f.data = rnn1.weight_f.clone().detach().to(device)
rnn3.weight_n.data = rnn1.weight_n.clone().detach().to(device)
rnn4 = HUBMGUCell(input_sz,
hidden_sz,
bias=bias,
weight_ext_f=rnn1.weight_f,
bias_ext_f=rnn1.bias_f,
weight_ext_n=rnn1.weight_n,
bias_ext_n=rnn1.bias_n,
hwcfg=hwcfg).to(device)
for i in range(win_sz):
hx1 = rnn1(input[i], hx1)
output1.append(hx1)
hx3 = rnn3(input[i], hx3)
output3.append(hx3)
hx4 = rnn4(input[i], hx4)
output4.append(hx4)
iVec, hVec = input[i], hx2
# rnn2 in the loop to mimic the hw reset
rnn2 = FSUMGUCell(input_sz,
hidden_sz,
bias=bias,
weight_ext_f=rnn1.weight_f,
bias_ext_f=rnn1.bias_f,
weight_ext_n=rnn1.weight_n,
bias_ext_n=rnn1.bias_n,
hx_buffer=hx2,
hwcfg=hwcfg,
swcfg=swcfg).to(device)
iSource = BinGen(iVec, hwcfg, swcfg)().to(device)
iRNG = RNG(hwcfg, swcfg)().to(device)
iBSG = BSGen(iSource, iRNG, swcfg).to(device)
iPE = ProgError(iVec, hwcfg).to(device)
hSource = BinGen(hVec, hwcfg, swcfg)().to(device)
hRNG = RNG(hwcfg, swcfg)().to(device)
hBSG = BSGen(hSource, hRNG, swcfg).to(device)
hPE = ProgError(hVec, hwcfg).to(device)
oVec = output1[i]
oPE = ProgError(oVec, hwcfg).to(device)
fg_ug_in_PE = ProgError(rnn1.fg_ug_in, hwcfg).to(device)
fg_in_PE = ProgError(rnn1.fg_in, hwcfg).to(device)
fg_PE = ProgError(rnn1.fg, hwcfg).to(device)
fg_hx_PE = ProgError(rnn1.fg_hx, hwcfg).to(device)
ng_ug_in_PE = ProgError(rnn1.ng_ug_in, hwcfg).to(device)
ng_PE = ProgError(rnn1.ng, hwcfg).to(device)
fg_ng_PE = ProgError(rnn1.fg_ng, hwcfg).to(device)
fg_ng_inv_PE = ProgError(rnn1.fg_ng_inv, hwcfg).to(device)
for c in range(2**bitwidth):
idx = torch.zeros(iSource.size()).type(torch.long).to(device)
iBS = iBSG(idx + c)
iPE.Monitor(iBS)
hdx = torch.zeros(hSource.size()).type(torch.long).to(device)
hBS = hBSG(hdx + c)
hPE.Monitor(hBS)
start_time = time.time()
oBS = rnn2(iBS, hBS)
fg_ug_in_PE.Monitor(rnn2.fg_ug_in)
fg_in_PE.Monitor(rnn2.fg_in)
fg_PE.Monitor(rnn2.fg)
fg_hx_PE.Monitor(rnn2.fg_hx)
ng_ug_in_PE.Monitor(rnn2.ng_ug_in)
ng_PE.Monitor(rnn2.ng)
fg_ng_PE.Monitor(rnn2.fg_ng)
fg_ng_inv_PE.Monitor(rnn2.fg_ng_inv)
oPE.Monitor(oBS)
hx2 = oPE()[0]
output2.append(hx2)
# print("======>> window: " + str(i) + "<<======")
# print("--- %s seconds ---" % (time.time() - start_time))
if output_error_only:
pass
else:
progerror_report(iPE, "input")
progerror_report(hPE, "hidden")
progerror_report(fg_ug_in_PE, "fg_ug_in")
progerror_report(fg_in_PE, "fg_in")
progerror_report(fg_PE, "fg")
progerror_report(fg_hx_PE, "fg_hx")
progerror_report(ng_ug_in_PE, "ng_ug_in")
progerror_report(ng_PE, "ng")
progerror_report(fg_ng_PE, "fg_ng")
progerror_report(fg_ng_inv_PE, "fg_ng_inv")
progerror_report(oPE, str(i) + "-th win output fsu")
hub_err = hx1 - hx4
min = hub_err.min().item()
max = hub_err.max().item()
rmse = torch.sqrt(torch.mean(torch.square(hub_err)))
std, mean = torch.std_mean(hub_err)
print("{:30s}".format(str(i)+"-th win output hub") + \
", Absolute Error range," + "{:12f}".format(min) + ", {:12f}".format(max) + \
", std," + "{:12f}".format(std) + \
", mean," + "{:12f}".format(mean) + \
", rmse," + "{:12f}".format(rmse))
fxp_err = hx1 - hx3
min = fxp_err.min().item()
max = fxp_err.max().item()
rmse = torch.sqrt(torch.mean(torch.square(fxp_err)))
std, mean = torch.std_mean(fxp_err)
print("{:30s}".format(str(i)+"-th win output fxp") + \
", Absolute Error range," + "{:12f}".format(min) + ", {:12f}".format(max) + \
", std," + "{:12f}".format(std) + \
", mean," + "{:12f}".format(mean) + \
", rmse," + "{:12f}".format(rmse))
print()
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
bias=True,
padding_mode='zeros',
weight_ext=None,
bias_ext=None,
hwcfg={
"width": 8,
"mode": "bipolar",
"rng": "Sobol",
"dimr": 1
},
swcfg={
"btype": torch.float,
"rtype": torch.float,
"stype": torch.float
}):
super(FSUConv2dPC, self).__init__(in_channels,
out_channels,
kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
groups=groups,
bias=bias,
padding_mode=padding_mode)
self.hwcfg = {}
self.hwcfg["width"] = hwcfg["width"]
self.hwcfg["mode"] = hwcfg["mode"].lower()
self.hwcfg["rng"] = hwcfg["rng"].lower()
self.hwcfg["dimr"] = hwcfg["dimr"]
self.swcfg = {}
self.swcfg["btype"] = swcfg["btype"]
self.swcfg["rtype"] = swcfg["rtype"]
self.swcfg["stype"] = swcfg["stype"]
self.mode = hwcfg["mode"].lower()
assert self.mode in ["unipolar", "bipolar"], \
"Error: the hw config 'mode' in " + str(self) + " class requires one of ['unipolar', 'bipolar']."
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = kernel_size
self.stride = stride
self.padding = padding
self.dilation = dilation
assert groups == 1, \
"Error: the 'groups' in " + str(self) + " class requires to be 1."
assert padding_mode == 'zeros', \
"Error: the 'padding_mode' in " + str(self) + " class requires to be 'zeros'."
# bias indication for original linear layer
self.has_bias = bias
# RNG for weight
hwcfg_wrng = {
"width": hwcfg["width"],
"rng": hwcfg["rng"],
"dimr": hwcfg["dimr"]
}
self.wrng = RNG(hwcfg_wrng, swcfg)()
if hwcfg["rng"].lower() in ["race", "tc", "race10", "tc10"]:
self.wtc = True
else:
self.wtc = False
# define the linear weight and bias
if weight_ext is not None:
assert (weight_ext.size()[0], weight_ext.size()[1], weight_ext.size()[2], weight_ext.size()[3]) == (out_channels, in_channels, num2tuple(kernel_size)[0], num2tuple(kernel_size)[1]), \
"Error: the hw config 'out_channels, in_channels, kernel_size' in " + str(self) + " class unmatches the binary weight shape."
self.weight.data = BinGen(weight_ext, self.hwcfg, self.swcfg)()
if bias and (bias_ext is not None):
assert bias_ext.size()[0] == out_channels, \
"Error: the hw config 'out_channels' in " + str(self) + " class unmatches the binary bias shape."
self.bias.data = BinGen(bias_ext, self.hwcfg, self.swcfg)()
# RNG for bias, same as RNG for weight
hwcfg_brng = {
"width": hwcfg["width"],
"rng": hwcfg["rng"],
"dimr": hwcfg["dimr"]
}
self.brng = RNG(hwcfg_brng, swcfg)()
# define the kernel linear for input bit 1
self.wbsg_i1 = BSGen(self.weight.view(1,
self.weight.size()[0], -1),
self.wrng, swcfg)
self.wrdx_i1 = torch.nn.Parameter(torch.zeros_like(self.weight,
dtype=torch.long),
requires_grad=False).view(
1,
self.weight.size()[0], -1)
if self.has_bias is True:
self.bbsg = BSGen(self.bias, self.brng, swcfg)
self.brdx = torch.nn.Parameter(torch.zeros_like(self.bias,
dtype=torch.long),
requires_grad=False)
# if bipolar, define a kernel for input bit 0, note that there is no bias required for this kernel
if (self.mode == "bipolar") and (self.wtc is False):
self.wbsg_i0 = BSGen(
self.weight.view(1,
self.weight.size()[0], -1), self.wrng, swcfg)
self.wrdx_i0 = torch.nn.Parameter(
torch.zeros_like(self.weight, dtype=torch.long),
requires_grad=False).view(1,
self.weight.size()[0], -1)
# indicator of even/odd cycle
self.even_cycle_flag = torch.nn.Parameter(torch.ones(1,
dtype=torch.bool),
requires_grad=False)
self.padding_0 = torch.nn.ConstantPad2d(self.padding, 0)
self.padding_1 = torch.nn.ConstantPad2d(self.padding, 1)
self.bipolar_mode = torch.nn.Parameter(torch.tensor(
[self.mode == "bipolar"], dtype=torch.bool),
requires_grad=False)