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README
Apache-2.0

EMLL - 高性能端侧机器学习计算库

EMLL(Edge ML Library)为加速终端侧设备上机器学习的推理而设计,提供基于端侧处理器的高性能机器学习计算函数库。EMLL支持fp32、fp16、int8等数据类型,已在有道词典笔、翻译王和超级词典等硬件产品的机器翻译和语音识别引擎中应用,大幅降低了推理延迟。

特点

高性能

EMLL实现的矩阵乘法函数,为端侧人工智能中常见的扁平矩阵作了专门的优化,为各常见ARM处理器作了特定的优化。对于cortex-A35/A53/A55处理器,本库针对它们的流水线特点,使用了汇编级别的优化。

下面是单精度矩阵乘法的测试结果[1]

ARMv8A Cortex-A35 四线程 ARMv8A Cortex-A53 四线程
结果1 结果2

[1]矩阵乘法的通式为 C[MxN] = A[MxK] B[KxN];所列数据为全行主序和全列主序的最好性能。

易用性

EMLL使用的函数接口在参数设计上力求简洁直接,矩阵乘法去掉了不常用的LD*参数,矩阵和向量的传递通过指针和整数维度分别传递。本库的构建和运行不依赖第三方计算库。

扩展性

对于矩阵乘法和量化函数,EMLL 库提取了它们和架构无关的代码作为通用的宏,这些宏可以在支持新的CPU架构时大大节省所需的代码量。

EMLL 应用接口

EMLL提供基于 C 的接口,详情请见 Usage_ZH.md

函数类型 函数名称 函数参数
矩阵乘法 data_type + "gemm" 源矩阵排列顺序,各矩阵地址,M,N,K,beta,并行线程数
全连接层(单精度) "fc" src/weight/bias/output的地址,M,K,N,源矩阵排列顺序,(并行线程数)
量化 "quantize_" + "symmetric"/"asymmetric" + input_type + output_type 输入数组,输出数组,(输出零点值),缩放值,数组大小,输入范围
重量化 "requantize_" + "symmetric/asymmetric" + "_XtoY" 输入数组,输出数组,(输出零点值),输出缩放值,数组大小,输入范围
偏置 "bias" + data_type 被偏置的矩阵,标量偏置,平行于主方向的向量偏置,平行于次方向的向量偏置,矩阵大小

各函数支持的数据类型

处理器 矩阵乘法 偏置 量化 重量化
ARMv7a 32-bit fp32,(u)int8 fp32,int32 fp32 -> (u)int16/(u)int8 int32 -> (u)int16/(u)int8,int16 -> (u)int8
ARMv8a 64-bit fp32,fp16,(u)int8 fp32,int32 fp32 -> (u)int16/(u)int8 int32 -> (u)int16/(u)int8,int16 -> (u)int8

EMLL 支持在 Linux 和安卓系统上运行。

EMLL 支持用 GCC 和 Clang 编译。

展望

EMLL 将来会根据需求,增加对端侧 GPU 和 NPU 的支持,并拓展支持的算子范围(卷积、激活函数等)。

许可证

Apache 2.0

参考

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

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

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简介

EMLL(Edge ML Library)为加速终端侧设备上机器学习的推理而设计,提供基于端侧处理器的高性能机器学习计算函数库 展开 收起
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