582 Tiny Neural Network Accelerator :: Quicker, easier and cheaper to make your own chip!

How it works

This project is a neural network accelerator designed for use with convolutional neural networks. The verilog is generated from system verilog source which lives in a separate repository: https://github.com/GregAC/tiny-nn which also contains the full DV environment, model, documentation and related utilities and software.

Internally it contains a number of 16-bit floating point add and multiply units (using something approximating the BF16 floating point encoding) that can be configured to work in different ways for different operations. Operations available are

Convolve - Computes a 4x2 convolution kernel across an image. The parameters are loaded in and held in flops then the image is streamed in one pixel at a time. The most recent 4x2 image pixels are also held in flops so every 2 new pixels giving a new image column computes a new convolution (internally the last image column is dropped and the new column shifted in).
Accumulate - Sum groups of N input numbers with a fixed bias added and an optional RELU operation (0 if accumulation less than 0 otherwise leaves accumulation untouched). N is provided by the operation word and the bias is only loaded in once. E.g. if you set N = 4 an bias of 1.0 for each 4 numbers input it would sum them together then add the bias and do the optional RELU. Numbers keep streaming in until the operation is terminated

The interface is a fixed 16 bits in and 8 bits out synchronous to the clock. Each operation has a special operand code that starts it that needs to be sent on the 16 input bits. Once started the 16 input bits provide the numbers used for the operation.

The 16-bit numbers output are split over 2 clock cycles for the 8 bit output. With the lower byte output first. The user needs to know when the output is relevant (some cycles the output should be ignored and some it should be captured).

ui_in, uio_in - 16-bit input, ui_in is top byte
uo_out - 8-bit output

https://github.com/GregAC/tiny-nn should contain full documentation with the details and software to use the accelerator (both a work in progress at tapeout time!).

How to test

There are 3 test modes to test basic input output.

ASCII test

Place 16'hFFFF on the input {ui_in, uio_in} and hold it and on the output you will observe a repeating pattern:

8'h54
8'h2d
8'h4e
8'h4e

This is 'T-NN' in ASCII

Pulse Test

Place 16'hF000 on the input {ui_in, uio_in} and hold it and on the output you will observe a repeating pattern:

8'haa
8'h55

Count Test

Place 16'hF1XX on the input {ui_in, uio_in} where XX is any 8-bit number and on the output you will observe a count down from that number.

Accumulate Operation

The simplest operation is the accumulate one. We'll configure it to add two numbers at a time with a -3.5 bias and RELU. Then we'll add 1.0 + 2.0 and 3.0 + 4.0. Put the following on the input over successive clocks

16'h2101 # Command word for accumulate operation
16'hc060 # -3.5 bias
16'h3f80 # 1.0
16'h4000 # 2.0
16'h4040 # 3.0
16'h4080 # 4.0
16'hffff # NaN - terminates operation

On the output you should observe:

16'hX
16'hX
16'hX
16'hX
16'hX
16'hX
16'hX
16'h00
16'h00
16'h60
16'h40

The 16'hX outputs could be anything and should be ignored, the first number output is 0000 representing 0.0 RELU(1.0 + 2.0 - 3.5) = 0.0, the second number output is 4060 representing 3.5 RELU(3.0 + 4.0 - 3.5) = 3.5.

External hardware

No specific external hardware required but it does need some external part to drive the desired sequences, this can be handled by the RP2040 on the demo board.

#	Input	Output	Bidirectional
0	ui[0]	uo[0]	uio[0]
1	ui[1]	uo[1]	uio[1]
2	ui[2]	uo[2]	uio[2]
3	ui[3]	uo[3]	uio[3]
4	ui[4]	uo[4]	uio[4]
5	ui[5]	uo[5]	uio[5]
6	ui[6]	uo[6]	uio[6]
7	ui[7]	uo[7]	uio[7]

582 Tiny Neural Network Accelerator