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Neural Network Kernel Library

About

We have implemented a library (lib_nn) of efficient neural network functions developed to maximize the performance and minimize the memory footprint of neural network inference on XMOS xcore.ai.

Required hardware

Only XS3 based microcontrollers are supported with this library. The previous generation XS1 and XS2 based microcontrollers are not supported.

XS3 based microcontrollers, like xcore.ai, have a vector unit with 256 bit wide registers and can operate in 8bit, 16bit or 32bit integer mode.

Prerequisites

This document assumes familiarity with the XMOS xCORE architecture, the XMOS tool chain, the 'C' programming language, and neural network concepts.

Building

For an XCore build:

mkdir -p build_xcore
cd build_xcore
cmake -DCMAKE_TOOLCHAIN_FILE=../etc/xmos_toolchain.cmake ..
make

For an x86 build:

mkdir -p build_x86
cd build_x86
cmake ..
make

API

The table below gives a quick overview of the APIs in lib_nn. Unless otherwise noted, all kernels below operate on signed 8-bit input and output tensors. The following symbols are used:

  • Cin - The number of input channels
  • Cout - The number of output channels
  • Kh - The kernel, filter or pool height
  • Kw - The kernel, filter or pool width
  • Sh - The stride height
  • Sw - The stride width

For full documentation of each API function, please refer to the description in the lib_nn/api/nn_operator.h header file.

Group API VPU Optimized Constraints Comments
Convolution
conv2d_deep Yes Cin % 4 = 0, Cout % 4 = 0
conv2d_shallowin Yes Cin % 4 = 0, Cout % 4 = 0, Cin * Kw = 32
conv2d_1x1 Yes Cin % 4 = 0, Cout % 4 = 0, Kh = Kw = 1, Sh = Sw = 1
conv2d_depthwise Yes Cin % 4 = 0, Cout % 4 = 0, Cin = Cout
Fully Connected
fully_connected_8 Yes Cin % 4 = 01 Output is 8-bit
fully_connected_16 Yes Cin % 4 = 01 Output is 16-bit
Pooling
maxpool2d Yes Cin % 4 = 0
avgpool2d Yes Cin % 4 = 0
avgpool2d_global Yes Cin % 4 = 0
Argmax
argmax_16 No Input is rank-1 Input is 16-bit
Activations
lookup8 No None Logistic (sigmoid), tanh & ReLU activation functions can be implemented using a look-up table mapping 8-bit inputs to 8-bit outputs
Misc
add_elementwise Yes None
requantize_16_to_8 Yes None Reduces the bit depth of a vector with 16-bit elements to a vector of 8-bit elements

1It is possible to relax this constraint. See the documentation for the API function.