And some more
diff --git a/xc7/main.cc b/xc7/main.cc
index cac9a39..4b3dec4 100644
--- a/xc7/main.cc
+++ b/xc7/main.cc
@@ -87,8 +87,6 @@
void Xc7CommandHandler::setupArchContext(Context *ctx)
{
- // if (vm.count("tmfuzz"))
- // ice40DelayFuzzerMain(ctx);
}
std::unique_ptr<Context> Xc7CommandHandler::createContext()
diff --git a/xc7/tmfuzz.py b/xc7/tmfuzz.py
deleted file mode 100644
index 4ec2a54..0000000
--- a/xc7/tmfuzz.py
+++ /dev/null
@@ -1,357 +0,0 @@
-#!/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"
-
-#%% Read fuzz data
-
-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()
-
- 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])
-
- 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:
- 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()
-
- 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(0):
- 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(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
-
-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()
-
-#%% 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)
- 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):
- 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)
-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()
-
-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.plot(delay_data[:, 0], delay_data[:, 5], ".")
-plt.plot(delay_map.flat, model1_map.flat, ".")
-plt.plot([0, max_delay], [0, max_delay], "k")
-plt.ylabel("Model #1 Delay")
-plt.xlabel("Actual Delay")
-plt.grid()
-plt.show()
-
-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()
-
-#%% Generate Model #2
-
-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):
- if delay_map_count[x, y] > 0:
- 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()
-
-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--")
