The LCLS2 Free Electron Laser FEL will generate xray pulses to beamline
experiments at up to 1Mhz These experimentals will require new ultrahigh rate
UHR detectors that can operate at rates above 100 kHz and generate data
throughputs upwards of 1 TBs a data velocity which requires prohibitively large
investments in storage infrastructure Machine Learning has demonstrated the
potential to digest large datasets to extract relevant insights however current
implementations show latencies that are too high for realtime data reduction
objectives SLAC has endeavored on the creation of a software framework which
translates MLs structures for deployment on Field Programmable Gate Arrays
FPGAs deployed at the Edge of the data chain close to the instrumentation This
framework leverages Xilinxs HLS framework presenting an API modeled after the
open source Keras interface to the TensorFlow library This SLAC Neural Network
Library SNL framework is designed with a streaming data approach optimizing the
data flow between layers while minimizing the buffer data buffering
requirements The goal is to ensure the highest possible framerate while keeping
the maximum latency constrained to the needs of the experiment Our framework is
designed to ensure the RTL implementation of the network layers supporting full
redeployment of weights and biases without requiring resynthesis after training
The ability to reduce the precision of the implemented networks through
quantization is necessary to optimize the use of both DSP and memory resources
in the FPGA We currently have a preliminary version of the toolset and are
experimenting with both general purpose example networks and networks being
designed for specific LCLS2 experiments.

本文介绍了用于解决 SLAC 数据实验的高速探测器的存储问题的解决方案 —— 用机器学习技术在边缘计算设备上实现实时数据处理的 SLAC 神经网络库框架，该框架采用基于 FPGA 的加速器来实现 ML 解决方案，并且支持所有权重的 RTL 重训练和恢复。