Enhancing Functional Safety in Automotive AMS Circuits through Unsupervised Machine Learning

no code implementations • 2 Apr 2024 • Ayush Arunachalam, Ian Kintz, Suvadeep Banerjee, Arnab Raha, Xiankun Jin, Fei Su, Viswanathan Pillai Prasanth, Rubin A. Parekhji, Suriyaprakash Natarajan, Kanad Basu

Our approach encompasses a systematic analysis of anomaly abstraction at multiple levels pertaining to the automotive domain, from hardware- to block-level, where anomalies are injected to create diverse fault scenarios.

Anomaly Detection

A Highly Configurable Hardware/Software Stack for DNN Inference Acceleration

no code implementations • 29 Nov 2021 • Suvadeep Banerjee, Steve Burns, Pasquale Cocchini, Abhijit Davare, Shweta Jain, Desmond Kirkpatrick, Anton Sorokin, Jin Yang, Zhenkun Yang

We have enhanced the VTA design space and enabled end-to-end support for additional workloads.

RHNAS: Realizable Hardware and Neural Architecture Search

no code implementations • 17 Jun 2021 • Yash Akhauri, Adithya Niranjan, J. Pablo Muñoz, Suvadeep Banerjee, Abhijit Davare, Pasquale Cocchini, Anton A. Sorokin, Ravi Iyer, Nilesh Jain

The rapidly evolving field of Artificial Intelligence necessitates automated approaches to co-design neural network architecture and neural accelerators to maximize system efficiency and address productivity challenges.

Neural Architecture Search