中文介绍

Edge ML Library - High-performance Compute Library for On-device Machine Learning Inference

Edge ML Library (EMLL) offers optimized basic routines like general matrix multiplications (GEMM) and quantizations, to speed up machine learning (ML) inference on ARM-based devices. EMLL supports fp32, fp16 and int8 data types. EMLL accelerates on-device NMT, ASR and OCR engines of Youdao, Inc.

Features

Performance-Oriented Design

The matrix-multiplication routines are heavily-optimized for matrix shapes common in on-device ML tasks, including “skinny” ones. The matrix-multiplication kernels are tuned for specific CPUs with a large portion of inline assembly codes.

Here are benchmarks of SGEMM on 2 machines^[1]:

armv8a cortex-A35 4-thread	armv8a cortex-A53 4-thread

[1].The fomular of GEMM: C[MxN] = A[MxK] B[KxN]; For each test case, the better performance in all-row-major and all-column-major situations is selected.

Facile Interface

The data and parameters are passed straightforward without wrappings. Matrices and arrays are passed with base address + dimensions. GEMM parameters seldom used in on-device inference like LDA-LDC are excluded from the interface. There is no dependency on any third-party compute libraries.

Extensibility

EMLL abstracts the core structures of CPU-based high-performance matrix multiplication algorithms and also bias/quant functions to general macros (see files under include/common), which can be applied to a variety of processors. When developing for a new architecture, a lot of coding works can be saved with these macros.

EMLL APIs

EMLL provides a series of C functions. See Usage_EN.md for details.

Type	Name	Parameters
Matrix Multiplication	data_type + “gemm”	matrix_orders, addresses of matrices, M, N, K, beta, number of threads
Fully-connect Layer (fp32)	“fc”	addresses of src/weight/bias/output, dimensions M/K/N, orders of source matrices, (number of threads)
Quantization	“quantize_” + “symmetric”/“asymmetric” + input_type + output_type	input array, output array, (zero point), scale, size of array, input range
Requantization	“requantize_” + “symmetric/asymmetric” + “_XtoY”	input array, output array, (zero point), output scale, size of array, input range
Bias	“bias” + data_type	the matrix to be biased, scalar bias to all elements, vector bias along major direction, vector bias along minor direction, dimensions of the matrix

Supported Architectures and Data Types

Target CPU	Matrix Multiplication	Bias	Quantization	Requantization
ARMv7a 32-bit	fp32 -> fp32, (u)int8 -> (u)int32	fp32, int32	fp32 -> (u)int8/(u)int16	int32 -> (u)int8/(u)int16, int16 -> (u)int8
ARMv8a 64-bit	fp32 -> fp32, (u)int8 -> (u)int32, fp16 -> fp16	fp32, fp16, int32	fp32 -> (u)int8/(u)int16	int32 -> (u)int8/(u)int16, int16 -> (u)int8

Supported OS: Linux & Android

Supported Compilers: GCC & Clang

Future Plan

EMLL may support on-device GPUs and NPUs in the future, with the expansion of available functions, according to business requirements.

License

Apache 2.0

Reference

Eigen: [https://eigen.tuxfamily.org]

OpenBLAS: [https://github.com/xianyi/OpenBLAS]