Peak Prediction Using Multi Layer Perceptron(MLP) for Edge Computing ASICs Targeting Scientific Applications

Sandeep Miryala1, Md Adnan Zaman2, Sandeep Mittal1, Yihui Ren1, Grzegorz Deptuch1, Gabriella Carini1, Sioan Zohar1, Shinjae Yoo1, Jack Fried1, Jin Huang1, Srinivas Katkoori2
1Brookhaven National Laboratory, 2University of South Florida


Abstract—High data rate detectors play an integral part in scientific research and their development is actively pursued at High Energy Physics (HEP) facilities around the world. Edge Machine Learning (ML) offers the ability to reduce data rates by integrating ML algorithms into Application Specific Integrated Circuits (ASICs) on the front end electronics. In this work, we explore a set of neural network architectures for predicting the peak amplitudes in the detector’s sensor response.We have designed and synthesized several MLP based neural networks comparing their inference accuracy, power consumption,and area targeting for minimal latency. The neural networks are synthesized in a commercial 65nm process. The effect of quantizing the network’s weights and biases on hardware performance and area is reported. We also conduct design space exploration to compare between design alternatives in terms of accuracy, power,performance, and area