TRNG done
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"execution_count": 73,
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"id": "5a87ea11-1c00-4097-a5b3-aa56cc33b7fb",
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"metadata": {},
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"(128, 750000)\n",
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"[59867 59631 64874 55734 51531 64641 63078 56084 62793 54154 50676 62981\n",
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" 55136 62640 58096 48365 62697 51194 54140 57243 59968 63117 45133 61542\n",
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" 59420 59012 61305 63438 58405 57121 62630 56219 54454 50854 65426 51115\n",
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" 47308 42314 54576 63390 62825 51672 51684 60670 51151 49354 51373 61212\n",
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" 57779 59093 61834 61733]\n",
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"True\n",
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"\n"
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]
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}
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],
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"source": [
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"import numpy as np\n",
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"\n",
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"TRNG_PAIR_CNT = 64\n",
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"\n",
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"\n",
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"if __name__ == '__main__':\n",
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" # reading info file - length of trace, sampling frequency (not necessary to know in our case), random value generated by the TRNG\n",
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" with open(\"data_info.txt\", \"r\") as fin:\n",
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" tracelen = int(fin.readline())\n",
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" fs = int(fin.readline())\n",
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" trng_val = fin.readline()\n",
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"\n",
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" traces = np.fromfile(\"data.bin\", dtype='uint8') # reading traces for individual ROs\n",
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" traces = np.reshape(traces, (traces.size//tracelen, tracelen)) # reshape of matrix, each row contains the trace for one RO\n",
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"\n",
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" traces_bin = traces > 128 # conversion of waveforms to rectangles - everything below threshold is 0, otherwise 1 (they are boolean values actually)\n",
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" print(traces)\n",
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" print(traces_bin.shape)\n",
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" rising_edges = np.logical_not(traces_bin[:,:-1]) & traces_bin[:,1:] # finding rising edges, each rising edge is represented by True\n",
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"\n",
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" cnt = np.count_nonzero(rising_edges, axis=1) # count the number of rising edges in rows\n",
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" \n",
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" # cnt is now a 1D vector\n",
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" cnt = cnt.reshape(TRNG_PAIR_CNT,2).min(axis=1) # Reshape of the count array into matrix, where each row contains 2 values - the number of rising edges for two ROs in a pair. Then we select the smaller value.\n",
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" \n",
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" cnt_sel = cnt & 0b00000011 # select only the two least significant bits\n",
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"\n",
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" estimate = ''.join([np.binary_repr(x, width=2) for x in cnt_sel]) # binary representation of the values (the last 2 bits) and joining them into one string\n",
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" estimate = '{0:0>32x}'.format(int(estimate, 2))\n",
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" # from data_info, output of the RNG in FPGA\n",
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" print(trng_val[:-1] == estimate)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7f8447e8-39c3-4706-902e-3970459ce515",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.15"
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"nbformat": 4,
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"nbformat_minor": 5
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100 000 cycles:
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implementation: OpenSSL Naive (4) With macro (5a) With T-Box (5b) Intrinsics (6)
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total time: 6.74264ms 23.6295ms 23.494ms 8.70669ms 1.18704ms
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avg time: 67.4264ns 236.295ns 234.94ns 87.0669ns 11.8704ns
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avg CPU cycles: 255 896 891 330 45
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1 000 000 cycles:
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implementation: OpenSSL Naive (4) With macro (5a) With T-Box (5b) Intrinsics (6)
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total time: 65.397ms 23.6295ms 233.56ms 88.0696ms 11.8984ms
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avg time: 65.397ns 236.295ns 233.56ns 88.0696ns 11.8984ns
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avg CPU cycles: 248 896 885 334 45
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"executionInfo": {
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"elapsed": 212,
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"status": "ok",
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"timestamp": 1733157570419,
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"user": {
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"displayName": "Ondřej Hladůvka",
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"userId": "07082375608847075000"
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},
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"user_tz": -60
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},
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"id": "qIH9E-egIPJk"
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},
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"outputs": [],
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"source": [
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"import os\n",
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"import numpy as np\n",
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"\n",
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"if not os.path.exists(\"chip1.bin\"):\n",
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" !wget https://users.fit.cvut.cz/bucekj/SRAM_data.zip\n",
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" !unzip SRAM_data.zip"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"executionInfo": {
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"elapsed": 340,
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"status": "ok",
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"timestamp": 1733157570996,
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"user": {
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"displayName": "Ondřej Hladůvka",
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"userId": "07082375608847075000"
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},
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"user_tz": -60
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},
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"id": "c6CaoHF7IVcf"
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},
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"outputs": [],
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"source": [
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"MEASUREMENTS = 1000\n",
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"MEM_SIZE = 512*8\n",
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"CHIPS = 10\n",
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"\n",
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"memories = np.array([\n",
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" np.unpackbits(\n",
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" np.fromfile(f\"chip{i+1}.bin\", dtype='uint8')\n",
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" ).reshape(MEASUREMENTS, MEM_SIZE)\n",
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" for i in range(CHIPS)\n",
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"])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"executionInfo": {
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"elapsed": 8,
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"status": "ok",
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"timestamp": 1733157570996,
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"user": {
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"displayName": "Ondřej Hladůvka",
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"userId": "07082375608847075000"
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},
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"user_tz": -60
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},
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"id": "IVARKdgpMOgI",
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"outputId": "1eb61f33-4c4d-46a4-aed1-ae467881f4de"
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(10, 1000, 4096)"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"memories.shape\n",
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"# cip, mereni, bit"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"executionInfo": {
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"elapsed": 7,
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"status": "ok",
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"timestamp": 1733157570996,
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"user": {
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"displayName": "Ondřej Hladůvka",
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"userId": "07082375608847075000"
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},
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"user_tz": -60
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},
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"id": "Iba9M10hIZql"
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},
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"outputs": [],
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"source": [
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"# Create reference responses\n",
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"avgs = memories.mean(axis=1, keepdims=True)\n",
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"refs = avgs>0.5 # reference responses"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"executionInfo": {
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"elapsed": 7,
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"status": "ok",
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"timestamp": 1733157570996,
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"user": {
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"displayName": "Ondřej Hladůvka",
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"userId": "07082375608847075000"
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},
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"user_tz": -60
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},
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"id": "0elUoltMIbZW"
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},
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"outputs": [],
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"source": [
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"xors = refs ^ memories # HDintra\n",
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" # finish HDintra calculation (look at the definition)\n",
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"\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"executionInfo": {
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"elapsed": 7,
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"status": "ok",
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"timestamp": 1733157570996,
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"user": {
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"displayName": "Ondřej Hladůvka",
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"userId": "07082375608847075000"
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},
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"user_tz": -60
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},
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"id": "2orRDbEnIdKh"
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},
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"outputs": [],
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"source": [
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"idxs = np.triu_indices(CHIPS, 1)\n",
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"pairwise_xors = refs[idxs[0]] ^ refs[idxs[1]]\n",
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"inter = pairwise_xors.mean()\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"executionInfo": {
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"elapsed": 7,
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"status": "ok",
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"timestamp": 1733157570996,
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"user": {
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"displayName": "Ondřej Hladůvka",
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"userId": "07082375608847075000"
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},
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"user_tz": -60
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},
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"id": "M4maPUbhIfF2"
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},
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"outputs": [],
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"source": [
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"# Create masks\n",
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"e = 0.05 # error rate into the mask (5%)\n",
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"masks = (avgs > (1-e)) | (avgs < e)\n",
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"masks = masks[:,0,:]\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {
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"executionInfo": {
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"elapsed": 7,
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"status": "ok",
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"timestamp": 1733157570996,
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"user": {
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"displayName": "Ondřej Hladůvka",
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"userId": "07082375608847075000"
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},
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"user_tz": -60
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},
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"id": "JQaOfv7Jin6S"
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},
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"outputs": [],
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"source": [
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"# Save masks into file\n",
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"np.savetxt('masks.txt', masks.astype(int), fmt='%d', delimiter='')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {
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"executionInfo": {
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"elapsed": 7,
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"status": "ok",
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"timestamp": 1733157570996,
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"user": {
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"displayName": "Ondřej Hladůvka",
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"userId": "07082375608847075000"
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},
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"user_tz": -60
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},
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"id": "qsDh1_3DImCW"
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},
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"outputs": [],
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"source": [
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"# Create masked responses (1024 bits long)\n",
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"mask_idx = np.array([np.nonzero(masks[i])[0][:1024] for i in range(CHIPS)])\n",
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"memories_masked = np.array([memories[i][:,mask_idx[i]] for i in range(CHIPS)])\n",
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"refs_masked = np.array([refs[i][:,mask_idx[i]] for i in range(CHIPS)])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {
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"executionInfo": {
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"elapsed": 7,
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"status": "ok",
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"timestamp": 1733157570996,
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"user": {
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"displayName": "Ondřej Hladůvka",
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"userId": "07082375608847075000"
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},
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"user_tz": -60
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},
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"id": "IhsqiP0WIqTe"
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},
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"outputs": [],
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"source": [
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"# Compute HDintra of masked PUF responses\n",
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"masked_xors = refs_masked ^ memories_masked # HDintra\n",
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" # finish HDintra calculation (look at the definition)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {
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"executionInfo": {
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"elapsed": 7,
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"status": "ok",
|
||||||
|
"timestamp": 1733157570996,
|
||||||
|
"user": {
|
||||||
|
"displayName": "Ondřej Hladůvka",
|
||||||
|
"userId": "07082375608847075000"
|
||||||
|
},
|
||||||
|
"user_tz": -60
|
||||||
|
},
|
||||||
|
"id": "S8omYwr1It2_"
|
||||||
|
},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"# Compute HDinter of masked PUF responses\n",
|
||||||
|
"idxs = np.triu_indices(CHIPS, 1)\n",
|
||||||
|
"pairwise_xors = refs_masked[idxs[0]] ^ refs_masked[idxs[1]]\n",
|
||||||
|
"masked_inter = pairwise_xors.mean()\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 12,
|
||||||
|
"metadata": {
|
||||||
|
"colab": {
|
||||||
|
"base_uri": "https://localhost:8080/"
|
||||||
|
},
|
||||||
|
"executionInfo": {
|
||||||
|
"elapsed": 297,
|
||||||
|
"status": "ok",
|
||||||
|
"timestamp": 1733157721196,
|
||||||
|
"user": {
|
||||||
|
"displayName": "Ondřej Hladůvka",
|
||||||
|
"userId": "07082375608847075000"
|
||||||
|
},
|
||||||
|
"user_tz": -60
|
||||||
|
},
|
||||||
|
"id": "jB0oHd-Mjkbx",
|
||||||
|
"outputId": "75f811c4-4a3f-420a-8bb0-3a9b54bb94a9"
|
||||||
|
},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"Vzdalenosti jednotlivych cipu: [0.03431421 0.02955688 0.0316062 0.03096167 0.0320459 0.02866528\n",
|
||||||
|
" 0.0269895 0.03107129 0.03059814 0.02904199]\n",
|
||||||
|
"\n",
|
||||||
|
"HDintra prumerny: 0.030485107421875\n",
|
||||||
|
"HDinter: 0.43573133680555554\n",
|
||||||
|
"\n",
|
||||||
|
"vyhovujicich bitu je 0.8744384765625 %\n",
|
||||||
|
"\n",
|
||||||
|
"Masked vzdalenosti jednotlivych cipu: [0.00191406 0.0017832 0.00191211 0.00174609 0.00198633 0.00169141\n",
|
||||||
|
" 0.0015 0.00189844 0.00163672 0.00171484]\n",
|
||||||
|
"\n",
|
||||||
|
"Masked HDintra prumerny: 0.0017783203125\n",
|
||||||
|
"Masked HDinter: 0.4957682291666667\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"# Print a summary of results (HDintra, HDinter - for both unmasked and masked PUF responses)\n",
|
||||||
|
"print(\"Vzdalenosti jednotlivych cipu:\", xors.mean(axis=1).mean(axis=1))\n",
|
||||||
|
"print()\n",
|
||||||
|
"\n",
|
||||||
|
"print(\"HDintra prumerny:\", xors.mean())\n",
|
||||||
|
"print(\"HDinter: \", inter)\n",
|
||||||
|
"print()\n",
|
||||||
|
"\n",
|
||||||
|
"print(\"vyhovujicich bitu je \", masks.mean(), \"%\")\n",
|
||||||
|
"print()\n",
|
||||||
|
"\n",
|
||||||
|
"print(\"Masked vzdalenosti jednotlivych cipu: \", masked_xors.mean(axis=1).mean(axis=1))\n",
|
||||||
|
"print()\n",
|
||||||
|
"\n",
|
||||||
|
"print(\"Masked HDintra prumerny:\", masked_xors.mean())\n",
|
||||||
|
"\n",
|
||||||
|
"print(\"Masked HDinter: \", masked_inter)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"metadata": {
|
||||||
|
"executionInfo": {
|
||||||
|
"elapsed": 2,
|
||||||
|
"status": "ok",
|
||||||
|
"timestamp": 1733157571185,
|
||||||
|
"user": {
|
||||||
|
"displayName": "Ondřej Hladůvka",
|
||||||
|
"userId": "07082375608847075000"
|
||||||
|
},
|
||||||
|
"user_tz": -60
|
||||||
|
},
|
||||||
|
"id": "ZFVMMBQ8PFBc"
|
||||||
|
},
|
||||||
|
"outputs": [],
|
||||||
|
"source": []
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"metadata": {
|
||||||
|
"colab": {
|
||||||
|
"provenance": [
|
||||||
|
{
|
||||||
|
"file_id": "https://gist.github.com/kodytfil/ca2be4dfc2413f2335451994308fb3c5#file-puf_template-ipynb",
|
||||||
|
"timestamp": 1733152912568
|
||||||
|
}
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"kernelspec": {
|
||||||
|
"display_name": "Python 3 (ipykernel)",
|
||||||
|
"language": "python",
|
||||||
|
"name": "python3"
|
||||||
|
},
|
||||||
|
"language_info": {
|
||||||
|
"codemirror_mode": {
|
||||||
|
"name": "ipython",
|
||||||
|
"version": 3
|
||||||
|
},
|
||||||
|
"file_extension": ".py",
|
||||||
|
"mimetype": "text/x-python",
|
||||||
|
"name": "python",
|
||||||
|
"nbconvert_exporter": "python",
|
||||||
|
"pygments_lexer": "ipython3",
|
||||||
|
"version": "3.10.15"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 4
|
||||||
|
}
|
||||||
Loading…
Reference in New Issue