358 lines
10 KiB
Python
358 lines
10 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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# ../nextpnr-ice40 --hx8k --tmfuzz > tmfuzz_hx8k.txt
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# ../nextpnr-ice40 --lp8k --tmfuzz > tmfuzz_lp8k.txt
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# ../nextpnr-ice40 --up5k --tmfuzz > tmfuzz_up5k.txt
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import numpy as np
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import matplotlib.pyplot as plt
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from collections import defaultdict
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device = "hx8k"
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# device = "lp8k"
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# device = "up5k"
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sel_src_type = "LUTFF_OUT"
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sel_dst_type = "LUTFF_IN_LUT"
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#%% Read fuzz data
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src_dst_pairs = defaultdict(lambda: 0)
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delay_data = list()
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all_delay_data = list()
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delay_map_sum = np.zeros((41, 41))
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delay_map_sum2 = np.zeros((41, 41))
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delay_map_count = np.zeros((41, 41))
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same_tile_delays = list()
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neighbour_tile_delays = list()
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type_delta_data = dict()
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with open("tmfuzz_%s.txt" % device, "r") as f:
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for line in f:
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line = line.split()
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if line[0] == "dst":
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dst_xy = (int(line[1]), int(line[2]))
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dst_type = line[3]
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dst_wire = line[4]
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src_xy = (int(line[1]), int(line[2]))
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src_type = line[3]
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src_wire = line[4]
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delay = int(line[5])
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estdelay = int(line[6])
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all_delay_data.append((delay, estdelay))
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src_dst_pairs[src_type, dst_type] += 1
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dx = dst_xy[0] - src_xy[0]
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dy = dst_xy[1] - src_xy[1]
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if src_type == sel_src_type and dst_type == sel_dst_type:
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if dx == 0 and dy == 0:
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same_tile_delays.append(delay)
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elif abs(dx) <= 1 and abs(dy) <= 1:
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neighbour_tile_delays.append(delay)
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else:
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delay_data.append((delay, estdelay, dx, dy, 0, 0, 0))
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relx = 20 + dst_xy[0] - src_xy[0]
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rely = 20 + dst_xy[1] - src_xy[1]
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if (0 <= relx <= 40) and (0 <= rely <= 40):
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delay_map_sum[relx, rely] += delay
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delay_map_sum2[relx, rely] += delay*delay
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delay_map_count[relx, rely] += 1
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if dst_type == sel_dst_type:
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if src_type not in type_delta_data:
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type_delta_data[src_type] = list()
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type_delta_data[src_type].append((dx, dy, delay))
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delay_data = np.array(delay_data)
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all_delay_data = np.array(all_delay_data)
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max_delay = np.max(delay_data[:, 0:2])
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mean_same_tile_delays = np.mean(neighbour_tile_delays)
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mean_neighbour_tile_delays = np.mean(neighbour_tile_delays)
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print("Avg same tile delay: %.2f (%.2f std, N=%d)" % \
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(mean_same_tile_delays, np.std(same_tile_delays), len(same_tile_delays)))
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print("Avg neighbour tile delay: %.2f (%.2f std, N=%d)" % \
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(mean_neighbour_tile_delays, np.std(neighbour_tile_delays), len(neighbour_tile_delays)))
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#%% Apply simple low-weight bluring to fill gaps
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for i in range(0):
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neigh_sum = np.zeros((41, 41))
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neigh_sum2 = np.zeros((41, 41))
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neigh_count = np.zeros((41, 41))
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for x in range(41):
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for y in range(41):
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for p in range(-1, 2):
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for q in range(-1, 2):
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if p == 0 and q == 0:
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continue
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if 0 <= (x+p) <= 40:
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if 0 <= (y+q) <= 40:
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neigh_sum[x, y] += delay_map_sum[x+p, y+q]
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neigh_sum2[x, y] += delay_map_sum2[x+p, y+q]
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neigh_count[x, y] += delay_map_count[x+p, y+q]
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delay_map_sum += 0.1 * neigh_sum
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delay_map_sum2 += 0.1 * neigh_sum2
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delay_map_count += 0.1 * neigh_count
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delay_map = delay_map_sum / delay_map_count
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delay_map_std = np.sqrt(delay_map_count*delay_map_sum2 - delay_map_sum**2) / delay_map_count
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#%% Print src-dst-pair summary
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print("Src-Dst-Type pair summary:")
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for cnt, src, dst in sorted([(v, k[0], k[1]) for k, v in src_dst_pairs.items()]):
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print("%20s %20s %5d%s" % (src, dst, cnt, " *" if src == sel_src_type and dst == sel_dst_type else ""))
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print()
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#%% Plot estimate vs actual delay
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plt.figure(figsize=(8, 3))
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plt.title("Estimate vs Actual Delay")
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plt.plot(all_delay_data[:, 0], all_delay_data[:, 1], ".")
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plt.plot(delay_data[:, 0], delay_data[:, 1], ".")
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plt.plot([0, max_delay], [0, max_delay], "k")
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plt.ylabel("Estimated Delay")
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plt.xlabel("Actual Delay")
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plt.grid()
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plt.show()
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#%% Plot delay heatmap and std dev heatmap
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plt.figure(figsize=(9, 3))
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plt.subplot(121)
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plt.title("Actual Delay Map")
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plt.imshow(delay_map)
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plt.colorbar()
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plt.subplot(122)
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plt.title("Standard Deviation")
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plt.imshow(delay_map_std)
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plt.colorbar()
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plt.show()
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#%% Generate Model #0
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def nonlinearPreprocessor0(dx, dy):
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dx, dy = abs(dx), abs(dy)
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values = [1.0]
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values.append(dx + dy)
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return np.array(values)
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A = np.zeros((41*41, len(nonlinearPreprocessor0(0, 0))))
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b = np.zeros(41*41)
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index = 0
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for x in range(41):
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for y in range(41):
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if delay_map_count[x, y] > 0:
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A[index, :] = nonlinearPreprocessor0(x-20, y-20)
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b[index] = delay_map[x, y]
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index += 1
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model0_params, _, _, _ = np.linalg.lstsq(A, b)
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print("Model #0 parameters:", model0_params)
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model0_map = np.zeros((41, 41))
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for x in range(41):
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for y in range(41):
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v = np.dot(model0_params, nonlinearPreprocessor0(x-20, y-20))
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model0_map[x, y] = v
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plt.figure(figsize=(9, 3))
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plt.subplot(121)
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plt.title("Model #0 Delay Map")
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plt.imshow(model0_map)
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plt.colorbar()
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plt.subplot(122)
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plt.title("Model #0 Error Map")
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plt.imshow(model0_map - delay_map)
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plt.colorbar()
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plt.show()
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for i in range(delay_data.shape[0]):
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dx = delay_data[i, 2]
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dy = delay_data[i, 3]
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delay_data[i, 4] = np.dot(model0_params, nonlinearPreprocessor0(dx, dy))
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plt.figure(figsize=(8, 3))
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plt.title("Model #0 vs Actual Delay")
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plt.plot(delay_data[:, 0], delay_data[:, 4], ".")
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plt.plot(delay_map.flat, model0_map.flat, ".")
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plt.plot([0, max_delay], [0, max_delay], "k")
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plt.ylabel("Model #0 Delay")
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plt.xlabel("Actual Delay")
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plt.grid()
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plt.show()
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print("In-sample RMS error: %f" % np.sqrt(np.nanmean((delay_map - model0_map)**2)))
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print("Out-of-sample RMS error: %f" % np.sqrt(np.nanmean((delay_data[:, 0] - delay_data[:, 4])**2)))
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print()
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#%% Generate Model #1
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def nonlinearPreprocessor1(dx, dy):
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dx, dy = abs(dx), abs(dy)
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values = [1.0]
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values.append(dx + dy) # 1-norm
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values.append((dx**2 + dy**2)**(1/2)) # 2-norm
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values.append((dx**3 + dy**3)**(1/3)) # 3-norm
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return np.array(values)
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A = np.zeros((41*41, len(nonlinearPreprocessor1(0, 0))))
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b = np.zeros(41*41)
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index = 0
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for x in range(41):
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for y in range(41):
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if delay_map_count[x, y] > 0:
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A[index, :] = nonlinearPreprocessor1(x-20, y-20)
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b[index] = delay_map[x, y]
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index += 1
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model1_params, _, _, _ = np.linalg.lstsq(A, b)
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print("Model #1 parameters:", model1_params)
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model1_map = np.zeros((41, 41))
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for x in range(41):
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for y in range(41):
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v = np.dot(model1_params, nonlinearPreprocessor1(x-20, y-20))
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model1_map[x, y] = v
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plt.figure(figsize=(9, 3))
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plt.subplot(121)
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plt.title("Model #1 Delay Map")
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plt.imshow(model1_map)
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plt.colorbar()
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plt.subplot(122)
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plt.title("Model #1 Error Map")
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plt.imshow(model1_map - delay_map)
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plt.colorbar()
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plt.show()
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for i in range(delay_data.shape[0]):
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dx = delay_data[i, 2]
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dy = delay_data[i, 3]
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delay_data[i, 5] = np.dot(model1_params, nonlinearPreprocessor1(dx, dy))
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plt.figure(figsize=(8, 3))
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plt.title("Model #1 vs Actual Delay")
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plt.plot(delay_data[:, 0], delay_data[:, 5], ".")
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plt.plot(delay_map.flat, model1_map.flat, ".")
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plt.plot([0, max_delay], [0, max_delay], "k")
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plt.ylabel("Model #1 Delay")
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plt.xlabel("Actual Delay")
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plt.grid()
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plt.show()
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print("In-sample RMS error: %f" % np.sqrt(np.nanmean((delay_map - model1_map)**2)))
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print("Out-of-sample RMS error: %f" % np.sqrt(np.nanmean((delay_data[:, 0] - delay_data[:, 5])**2)))
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print()
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#%% Generate Model #2
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def nonlinearPreprocessor2(v):
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return np.array([1, v, np.sqrt(v)])
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A = np.zeros((41*41, len(nonlinearPreprocessor2(0))))
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b = np.zeros(41*41)
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index = 0
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for x in range(41):
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for y in range(41):
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if delay_map_count[x, y] > 0:
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A[index, :] = nonlinearPreprocessor2(model1_map[x, y])
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b[index] = delay_map[x, y]
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index += 1
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model2_params, _, _, _ = np.linalg.lstsq(A, b)
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print("Model #2 parameters:", model2_params)
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model2_map = np.zeros((41, 41))
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for x in range(41):
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for y in range(41):
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v = np.dot(model1_params, nonlinearPreprocessor1(x-20, y-20))
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v = np.dot(model2_params, nonlinearPreprocessor2(v))
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model2_map[x, y] = v
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plt.figure(figsize=(9, 3))
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plt.subplot(121)
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plt.title("Model #2 Delay Map")
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plt.imshow(model2_map)
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plt.colorbar()
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plt.subplot(122)
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plt.title("Model #2 Error Map")
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plt.imshow(model2_map - delay_map)
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plt.colorbar()
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plt.show()
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for i in range(delay_data.shape[0]):
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dx = delay_data[i, 2]
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dy = delay_data[i, 3]
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delay_data[i, 6] = np.dot(model2_params, nonlinearPreprocessor2(delay_data[i, 5]))
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plt.figure(figsize=(8, 3))
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plt.title("Model #2 vs Actual Delay")
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plt.plot(delay_data[:, 0], delay_data[:, 6], ".")
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plt.plot(delay_map.flat, model2_map.flat, ".")
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plt.plot([0, max_delay], [0, max_delay], "k")
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plt.ylabel("Model #2 Delay")
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plt.xlabel("Actual Delay")
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plt.grid()
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plt.show()
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print("In-sample RMS error: %f" % np.sqrt(np.nanmean((delay_map - model2_map)**2)))
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print("Out-of-sample RMS error: %f" % np.sqrt(np.nanmean((delay_data[:, 0] - delay_data[:, 6])**2)))
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print()
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#%% Generate deltas for different source net types
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type_deltas = dict()
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print("Delay deltas for different src types:")
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for src_type in sorted(type_delta_data.keys()):
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deltas = list()
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for dx, dy, delay in type_delta_data[src_type]:
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dx = abs(dx)
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dy = abs(dy)
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if dx > 1 or dy > 1:
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est = model0_params[0] + model0_params[1] * (dx + dy)
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else:
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est = mean_neighbour_tile_delays
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deltas.append(delay - est)
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print("%15s: %8.2f (std %6.2f)" % (\
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src_type, np.mean(deltas), np.std(deltas)))
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type_deltas[src_type] = np.mean(deltas)
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#%% Print C defs of model parameters
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print("--snip--")
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print("%d, %d, %d," % (mean_neighbour_tile_delays, 128 * model0_params[0], 128 * model0_params[1]))
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print("%d, %d, %d, %d," % (128 * model1_params[0], 128 * model1_params[1], 128 * model1_params[2], 128 * model1_params[3]))
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print("%d, %d, %d," % (128 * model2_params[0], 128 * model2_params[1], 128 * model2_params[2]))
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print("%d, %d, %d, %d" % (type_deltas["LOCAL"], type_deltas["LUTFF_IN"], \
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(type_deltas["SP4_H"] + type_deltas["SP4_V"]) / 2,
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(type_deltas["SP12_H"] + type_deltas["SP12_V"]) / 2))
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print("--snap--")
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