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Title: Waveform processing using neural network algorithms on the front-end electronics

Journal Article · · Journal of Instrumentation
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  1. Brookhaven National Lab. (BNL), Upton, NY (United States)
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In a multi-channel radiation detector readout system, waveform sampling, digitization, and raw data transmission to the data acquisition system constitute a conventional processing chain. The deposited energy on the sensor is estimated by extracting peak amplitudes, area under pulse envelopes from the raw data, and starting times of signals or time of arrivals. However, such quantities can be estimated using machine learning algorithms on the front-end Application-Specific Integrated Circuits (ASICs), often termed as “edge computing”. Edge computation offers enormous benefits, especially when the analytical forms are not fully known or the registered waveform suffers from noise and imperfections of practical implementations. In this work, we aim to predict peak amplitude from a single waveform snippet whose rising and falling edges containing only 3 to 4 samples. We thoroughly studied two well-accepted neural network algorithms, Multi-Layer Perceptron (MLP) and Convolutional Neural Network (CNN) by varying their model sizes. Further, to better fit front-end electronics, neural network model reduction techniques, such as network pruning methods and variable-bit quantization approaches, were also studied. By combining pruning and quantization, our best performing model has the size of 1.5 KB, reduced from 16.6 KB of its full model counterpart. It can reach mean absolute error of 0.034 comparing to that of a naive baseline of 0.135. Such parameter-efficient and predictive neural network models established feasibility and practicality of their deployment on front-end ASICs.

Research Organization:
Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Organization:
USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Science (SC), Basic Energy Sciences (BES)
Grant/Contract Number:
SC0012704
OSTI ID:
1844566
Report Number(s):
BNL-222708-2022-JAAM
Journal Information:
Journal of Instrumentation, Journal Name: Journal of Instrumentation Journal Issue: 01 Vol. 17; ISSN 1748-0221
Publisher:
Institute of Physics (IOP)Copyright Statement
Country of Publication:
United States
Language:
English

References (5)

FPGA-Based Inter-layer Pipelined Accelerators for Filter-Wise Weight-Balanced Sparse Fully Convolutional Networks with Overlapped Tiling journal February 2021
Multilayer feedforward networks are universal approximators journal January 1989
Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference conference June 2018
A Reconfigurable Neural Network ASIC for Detector Front-End Data Compression at the HL-LHC journal August 2021
Ps and Qs: Quantization-Aware Pruning for Efficient Low Latency Neural Network Inference journal July 2021

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47 OTHER INSTRUMENTATION
Data processing methods
Data reduction methods
Front-end electronics for detector readout
Simulation methods and programs