330 ChatGPT designed Recurrent Spiking Neural Network

330 : ChatGPT designed Recurrent Spiking Neural Network

  • Author: Paola Vitolo, Michael Tomlinson, ChatGPT-4, Gian Domenico Licciardo, Andreas Andreou - [email protected]
  • Description: Programmable recurrent spiking neural network with 9 recurrent LIF neurons ( 3 input - 3 output ) with fully programmable weights (8-bit)
  • GitHub repository
  • Clock: 0 Hz

How it works

This project implements 9 programmable digital recurrent LIF neurons. The neurons are arranged in 3 layers (3 in each). Spikes_in directly maps to the inputs of the first layer neurons. When an input spike is received, it is first multiplied by an 8 bit weight, programmable from a custom interface, 1 per input neuron. This 8 bit value is then added to the membrane potential of the respective neuron. When the first layer neurons activate, its pulse is routed to each of the 3 neurons in the next layer. There are 9x3 programmable weights describing the connectivity between the input spikes and the first layer (9 weights=3x3), the first and second layers (9 weights=3x3), and second and third layers (9 weights=3x3). Output spikes from the 3nd layer drive spikes_out.

How to test

After reset, program the neuron threshold, leak rate, feedback_scale and refractory period. Additionally program the first, 2nd, 3rd layer weights. Once programmed activate spikes_in to represent input data, track spikes_out synchronously (1 clock cycle pulses).

External hardware

List external hardware used in your project (e.g. PMOD, LED display, etc), if any

Memory

The memory block stores 39 words of 8 bits. The first 12 words represent Feedback scale, Refractory Period, Decay rate, Membrane Threshold of each layer ( 4 parameters x 3 layers ). The last 27 words represent the network weights ( 3 ins x 3 neurons x 3 layers ).

Address Address Data Index Data Index Data Description Data Description
dec hex MSB LSB Description LYR #
0 0 7 0 Feedback scale 1
1 1 15 8 Refractory Period 1
2 2 23 16 Decay rate 1
3 3 31 24 Membrane Threshold 1
4 4 39 32 Feedback scale 2
5 5 47 40 Refractory Period 2
6 6 55 48 Decay rate 2
7 7 63 56 Membrane Threshold 2
8 8 71 64 Feedback scale 3
9 9 79 72 Refractory Period 3
10 A 87 80 Decay rate 3
11 B 95 88 Membrane Threshold 3
12 C 103 96 weight1_0 3
13 D 111 104 weight1_1 3
14 E 119 112 weight1_2 3
15 F 127 120 weight2_0 3
16 10 135 128 weight2_1 3
17 11 143 136 weight2_2 3
18 12 151 144 weight3_0 3
19 13 159 152 weight3_1 3
20 14 167 160 weight3_2 3
21 15 175 168 weight1_0 2
22 16 183 176 weight1_1 2
23 17 191 184 weight1_2 2
24 18 199 192 weight2_0 2
25 19 207 200 weight2_1 2
26 1A 215 208 weight2_2 2
27 1B 223 216 weight3_0 2
28 1C 231 224 weight3_1 2
29 1D 239 232 weight3_2 2
30 1E 247 240 weight1_0 1
31 1F 255 248 weight1_1 1
32 20 263 256 weight1_2 1
33 21 271 264 weight2_0 1
34 22 279 272 weight2_1 1
35 23 287 280 weight2_2 1
36 24 295 288 weight3_0 1
37 25 303 296 weight3_1 1
38 26 311 304 weight3_2 1

ChatGPT Transcripts:

IO

#InputOutputBidirectional
0Input Spike 0Output Spike 0out_test 0
1Input Spike 1Output Spike 1out_test 1
2Input Spike 2Output Spike 2out_test 2
3Spike Input Register EnableEnd of Writing Parameters into Memoryout_test 3
4RSNN enableParameter Data Writtenout_test 4
5Serial Data INout_test 5
6Parameter Loadout_test 6
7Test selectionout_test 7

Chip location

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