diff options
Diffstat (limited to 'ice40/tmfuzz.py')
| -rw-r--r-- | ice40/tmfuzz.py | 268 |
1 files changed, 212 insertions, 56 deletions
diff --git a/ice40/tmfuzz.py b/ice40/tmfuzz.py index caf3bc80..4ec2a546 100644 --- a/ice40/tmfuzz.py +++ b/ice40/tmfuzz.py @@ -1,12 +1,17 @@ #!/usr/bin/env python3 # -*- coding: utf-8 -*- # ../nextpnr-ice40 --hx8k --tmfuzz > tmfuzz_hx8k.txt +# ../nextpnr-ice40 --lp8k --tmfuzz > tmfuzz_lp8k.txt +# ../nextpnr-ice40 --up5k --tmfuzz > tmfuzz_up5k.txt import numpy as np import matplotlib.pyplot as plt from collections import defaultdict device = "hx8k" +# device = "lp8k" +# device = "up5k" + sel_src_type = "LUTFF_OUT" sel_dst_type = "LUTFF_IN_LUT" @@ -15,10 +20,17 @@ sel_dst_type = "LUTFF_IN_LUT" src_dst_pairs = defaultdict(lambda: 0) delay_data = list() +all_delay_data = list() + delay_map_sum = np.zeros((41, 41)) delay_map_sum2 = np.zeros((41, 41)) delay_map_count = np.zeros((41, 41)) +same_tile_delays = list() +neighbour_tile_delays = list() + +type_delta_data = dict() + with open("tmfuzz_%s.txt" % device, "r") as f: for line in f: line = line.split() @@ -35,23 +47,52 @@ with open("tmfuzz_%s.txt" % device, "r") as f: delay = int(line[5]) estdelay = int(line[6]) + all_delay_data.append((delay, estdelay)) + src_dst_pairs[src_type, dst_type] += 1 + dx = dst_xy[0] - src_xy[0] + dy = dst_xy[1] - src_xy[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 dx == 0 and dy == 0: + same_tile_delays.append(delay) + + elif abs(dx) <= 1 and abs(dy) <= 1: + neighbour_tile_delays.append(delay) + + else: + delay_data.append((delay, estdelay, dx, dy, 0, 0, 0)) + + 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 + + if dst_type == sel_dst_type: + if src_type not in type_delta_data: + type_delta_data[src_type] = list() - 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 + type_delta_data[src_type].append((dx, dy, delay)) delay_data = np.array(delay_data) +all_delay_data = np.array(all_delay_data) +max_delay = np.max(delay_data[:, 0:2]) + +mean_same_tile_delays = np.mean(neighbour_tile_delays) +mean_neighbour_tile_delays = np.mean(neighbour_tile_delays) + +print("Avg same tile delay: %.2f (%.2f std, N=%d)" % \ + (mean_same_tile_delays, np.std(same_tile_delays), len(same_tile_delays))) +print("Avg neighbour tile delay: %.2f (%.2f std, N=%d)" % \ + (mean_neighbour_tile_delays, np.std(neighbour_tile_delays), len(neighbour_tile_delays))) #%% Apply simple low-weight bluring to fill gaps -for i in range(1): +for i in range(0): neigh_sum = np.zeros((41, 41)) neigh_sum2 = np.zeros((41, 41)) neigh_count = np.zeros((41, 41)) @@ -84,8 +125,14 @@ print() #%% Plot estimate vs actual delay -plt.figure() -plt.plot(delay_data[:,0], delay_data[:,1], ".") +plt.figure(figsize=(8, 3)) +plt.title("Estimate vs Actual Delay") +plt.plot(all_delay_data[:, 0], all_delay_data[:, 1], ".") +plt.plot(delay_data[:, 0], delay_data[:, 1], ".") +plt.plot([0, max_delay], [0, max_delay], "k") +plt.ylabel("Estimated Delay") +plt.xlabel("Actual Delay") +plt.grid() plt.show() #%% Plot delay heatmap and std dev heatmap @@ -101,7 +148,65 @@ plt.imshow(delay_map_std) plt.colorbar() plt.show() -#%% Linear least-squares fits of delayEstimate models +#%% Generate Model #0 + +def nonlinearPreprocessor0(dx, dy): + dx, dy = abs(dx), abs(dy) + values = [1.0] + values.append(dx + dy) + return np.array(values) + +A = np.zeros((41*41, len(nonlinearPreprocessor0(0, 0)))) +b = np.zeros(41*41) + +index = 0 +for x in range(41): + for y in range(41): + if delay_map_count[x, y] > 0: + A[index, :] = nonlinearPreprocessor0(x-20, y-20) + b[index] = delay_map[x, y] + index += 1 + +model0_params, _, _, _ = np.linalg.lstsq(A, b) +print("Model #0 parameters:", model0_params) + +model0_map = np.zeros((41, 41)) +for x in range(41): + for y in range(41): + v = np.dot(model0_params, nonlinearPreprocessor0(x-20, y-20)) + model0_map[x, y] = v + +plt.figure(figsize=(9, 3)) +plt.subplot(121) +plt.title("Model #0 Delay Map") +plt.imshow(model0_map) +plt.colorbar() +plt.subplot(122) +plt.title("Model #0 Error Map") +plt.imshow(model0_map - delay_map) +plt.colorbar() +plt.show() + +for i in range(delay_data.shape[0]): + dx = delay_data[i, 2] + dy = delay_data[i, 3] + delay_data[i, 4] = np.dot(model0_params, nonlinearPreprocessor0(dx, dy)) + +plt.figure(figsize=(8, 3)) +plt.title("Model #0 vs Actual Delay") +plt.plot(delay_data[:, 0], delay_data[:, 4], ".") +plt.plot(delay_map.flat, model0_map.flat, ".") +plt.plot([0, max_delay], [0, max_delay], "k") +plt.ylabel("Model #0 Delay") +plt.xlabel("Actual Delay") +plt.grid() +plt.show() + +print("In-sample RMS error: %f" % np.sqrt(np.nanmean((delay_map - model0_map)**2))) +print("Out-of-sample RMS error: %f" % np.sqrt(np.nanmean((delay_data[:, 0] - delay_data[:, 4])**2))) +print() + +#%% Generate Model #1 def nonlinearPreprocessor1(dx, dy): dx, dy = abs(dx), abs(dy) @@ -117,8 +222,9 @@ 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] + if delay_map_count[x, y] > 0: + A[index, :] = nonlinearPreprocessor1(x-20, y-20) + b[index] = delay_map[x, y] index += 1 model1_params, _, _, _ = np.linalg.lstsq(A, b) @@ -141,61 +247,111 @@ plt.imshow(model1_map - delay_map) plt.colorbar() plt.show() +for i in range(delay_data.shape[0]): + dx = delay_data[i, 2] + dy = delay_data[i, 3] + delay_data[i, 5] = np.dot(model1_params, nonlinearPreprocessor1(dx, dy)) + plt.figure(figsize=(8, 3)) -plt.title("Model #1 vs Actual Delay") +plt.title("Model #1 vs Actual Delay") +plt.plot(delay_data[:, 0], delay_data[:, 5], ".") plt.plot(delay_map.flat, model1_map.flat, ".") -plt.plot([0, 4000], [0, 4000], "k") +plt.plot([0, max_delay], [0, max_delay], "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("In-sample RMS error: %f" % np.sqrt(np.nanmean((delay_map - model1_map)**2))) +print("Out-of-sample RMS error: %f" % np.sqrt(np.nanmean((delay_data[:, 0] - delay_data[:, 5])**2))) print() -if True: - def nonlinearPreprocessor2(v): - return np.array([1, v, np.sqrt(v)]) +#%% Generate Model #2 - A = np.zeros((41*41, len(nonlinearPreprocessor2(0)))) - b = np.zeros(41*41) +def nonlinearPreprocessor2(v): + return np.array([1, v, np.sqrt(v)]) - index = 0 - for x in range(41): - for y in range(41): +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): + if delay_map_count[x, y] > 0: A[index, :] = nonlinearPreprocessor2(model1_map[x, y]) b[index] = delay_map[x, y] - index += 1 + index += 1 - model2_params, _, _, _ = np.linalg.lstsq(A, b) - print("Model #2 parameters:", model2_params) +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() +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() + +for i in range(delay_data.shape[0]): + dx = delay_data[i, 2] + dy = delay_data[i, 3] + delay_data[i, 6] = np.dot(model2_params, nonlinearPreprocessor2(delay_data[i, 5])) + +plt.figure(figsize=(8, 3)) +plt.title("Model #2 vs Actual Delay") +plt.plot(delay_data[:, 0], delay_data[:, 6], ".") +plt.plot(delay_map.flat, model2_map.flat, ".") +plt.plot([0, max_delay], [0, max_delay], "k") +plt.ylabel("Model #2 Delay") +plt.xlabel("Actual Delay") +plt.grid() +plt.show() + +print("In-sample RMS error: %f" % np.sqrt(np.nanmean((delay_map - model2_map)**2))) +print("Out-of-sample RMS error: %f" % np.sqrt(np.nanmean((delay_data[:, 0] - delay_data[:, 6])**2))) +print() + +#%% Generate deltas for different source net types + +type_deltas = dict() + +print("Delay deltas for different src types:") +for src_type in sorted(type_delta_data.keys()): + deltas = list() + + for dx, dy, delay in type_delta_data[src_type]: + dx = abs(dx) + dy = abs(dy) + + if dx > 1 or dy > 1: + est = model0_params[0] + model0_params[1] * (dx + dy) + else: + est = mean_neighbour_tile_delays + deltas.append(delay - est) + + print("%15s: %8.2f (std %6.2f)" % (\ + src_type, np.mean(deltas), np.std(deltas))) + + type_deltas[src_type] = np.mean(deltas) + +#%% Print C defs of model parameters + +print("--snip--") +print("%d, %d, %d," % (mean_neighbour_tile_delays, 128 * model0_params[0], 128 * model0_params[1])) +print("%d, %d, %d, %d," % (128 * model1_params[0], 128 * model1_params[1], 128 * model1_params[2], 128 * model1_params[3])) +print("%d, %d, %d," % (128 * model2_params[0], 128 * model2_params[1], 128 * model2_params[2])) +print("%d, %d, %d, %d" % (type_deltas["LOCAL"], type_deltas["LUTFF_IN"], \ + (type_deltas["SP4_H"] + type_deltas["SP4_V"]) / 2, + (type_deltas["SP12_H"] + type_deltas["SP12_V"]) / 2)) +print("--snap--") |