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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# ../nextpnr-ice40 --hx8k --tmfuzz > tmfuzz_hx8k.txt
import numpy as np
import matplotlib.pyplot as plt
from collections import defaultdict
device = "hx8k"
sel_src_type = "LUTFF_OUT"
sel_dst_type = "LUTFF_IN_LUT"
#%% Read fuzz data
src_dst_pairs = defaultdict(lambda: 0)
delay_data = list()
delay_map_sum = np.zeros((41, 41))
delay_map_sum2 = np.zeros((41, 41))
delay_map_count = np.zeros((41, 41))
with open("tmfuzz_%s.txt" % device, "r") as f:
for line in f:
line = line.split()
if line[0] == "dst":
dst_xy = (int(line[1]), int(line[2]))
dst_type = line[3]
dst_wire = line[4]
src_xy = (int(line[1]), int(line[2]))
src_type = line[3]
src_wire = line[4]
delay = int(line[5])
estdelay = int(line[6])
src_dst_pairs[src_type, dst_type] += 1
if src_type == sel_src_type and dst_type == sel_dst_type:
delay_data.append((delay, estdelay))
relx = 20 + dst_xy[0] - src_xy[0]
rely = 20 + dst_xy[1] - src_xy[1]
if (0 <= relx <= 40) and (0 <= rely <= 40):
delay_map_sum[relx, rely] += delay
delay_map_sum2[relx, rely] += delay*delay
delay_map_count[relx, rely] += 1
delay_data = np.array(delay_data)
#%% Apply simple low-weight bluring to fill gaps
for i in range(1):
neigh_sum = np.zeros((41, 41))
neigh_sum2 = np.zeros((41, 41))
neigh_count = np.zeros((41, 41))
for x in range(41):
for y in range(41):
for p in range(-1, 2):
for q in range(-1, 2):
if p == 0 and q == 0:
continue
if 0 <= (x+p) <= 40:
if 0 <= (y+q) <= 40:
neigh_sum[x, y] += delay_map_sum[x+p, y+q]
neigh_sum2[x, y] += delay_map_sum2[x+p, y+q]
neigh_count[x, y] += delay_map_count[x+p, y+q]
delay_map_sum += 0.1 * neigh_sum
delay_map_sum2 += 0.1 * neigh_sum2
delay_map_count += 0.1 * neigh_count
delay_map = delay_map_sum / delay_map_count
delay_map_std = np.sqrt(delay_map_count*delay_map_sum2 - delay_map_sum**2) / delay_map_count
#%% Print src-dst-pair summary
print("Src-Dst-Type pair summary:")
for cnt, src, dst in sorted([(v, k[0], k[1]) for k, v in src_dst_pairs.items()]):
print("%20s %20s %5d%s" % (src, dst, cnt, " *" if src == sel_src_type and dst == sel_dst_type else ""))
print()
#%% Plot estimate vs actual delay
plt.figure()
plt.plot(delay_data[:,0], delay_data[:,1], ".")
plt.show()
#%% Plot delay heatmap and std dev heatmap
plt.figure(figsize=(9, 3))
plt.subplot(121)
plt.title("Actual Delay Map")
plt.imshow(delay_map)
plt.colorbar()
plt.subplot(122)
plt.title("Standard Deviation")
plt.imshow(delay_map_std)
plt.colorbar()
plt.show()
#%% Linear least-squares fits of delayEstimate models
def nonlinearPreprocessor1(dx, dy):
dx, dy = abs(dx), abs(dy)
values = [1.0]
values.append(dx + dy) # 1-norm
values.append((dx**2 + dy**2)**(1/2)) # 2-norm
values.append((dx**3 + dy**3)**(1/3)) # 3-norm
return np.array(values)
A = np.zeros((41*41, len(nonlinearPreprocessor1(0, 0))))
b = np.zeros(41*41)
index = 0
for x in range(41):
for y in range(41):
A[index, :] = nonlinearPreprocessor1(x-20, y-20)
b[index] = delay_map[x, y]
index += 1
model1_params, _, _, _ = np.linalg.lstsq(A, b)
print("Model #1 parameters:", model1_params)
model1_map = np.zeros((41, 41))
for x in range(41):
for y in range(41):
v = np.dot(model1_params, nonlinearPreprocessor1(x-20, y-20))
model1_map[x, y] = v
plt.figure(figsize=(9, 3))
plt.subplot(121)
plt.title("Model #1 Delay Map")
plt.imshow(model1_map)
plt.colorbar()
plt.subplot(122)
plt.title("Model #1 Error Map")
plt.imshow(model1_map - delay_map)
plt.colorbar()
plt.show()
plt.figure(figsize=(8, 3))
plt.title("Model #1 vs Actual Delay")
plt.plot(delay_map.flat, model1_map.flat, ".")
plt.plot([0, 4000], [0, 4000], "k")
plt.ylabel("Model #1 Delay")
plt.xlabel("Actual Delay")
plt.grid()
plt.show()
print("Total RMS error: %f" % np.sqrt(np.mean((delay_map - model1_map)**2)))
print()
if True:
def nonlinearPreprocessor2(v):
return np.array([1, v, np.sqrt(v)])
A = np.zeros((41*41, len(nonlinearPreprocessor2(0))))
b = np.zeros(41*41)
index = 0
for x in range(41):
for y in range(41):
A[index, :] = nonlinearPreprocessor2(model1_map[x, y])
b[index] = delay_map[x, y]
index += 1
model2_params, _, _, _ = np.linalg.lstsq(A, b)
print("Model #2 parameters:", model2_params)
model2_map = np.zeros((41, 41))
for x in range(41):
for y in range(41):
v = np.dot(model1_params, nonlinearPreprocessor1(x-20, y-20))
v = np.dot(model2_params, nonlinearPreprocessor2(v))
model2_map[x, y] = v
plt.figure(figsize=(9, 3))
plt.subplot(121)
plt.title("Model #2 Delay Map")
plt.imshow(model2_map)
plt.colorbar()
plt.subplot(122)
plt.title("Model #2 Error Map")
plt.imshow(model2_map - delay_map)
plt.colorbar()
plt.show()
plt.figure(figsize=(8, 3))
plt.title("Model #2 vs Actual Delay")
plt.plot(delay_map.flat, model2_map.flat, ".")
plt.plot([0, 4000], [0, 4000], "k")
plt.ylabel("Model #2 Delay")
plt.xlabel("Actual Delay")
plt.grid()
plt.show()
print("Total RMS error: %f" % np.sqrt(np.mean((delay_map - model2_map)**2)))
print()
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