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

FlexNN: A Dataflow-aware Flexible Deep Learning Accelerator for Energy-Efficient Edge Devices

no code implementations • 14 Mar 2024 • Arnab Raha, Deepak A. Mathaikutty, Soumendu K. Ghosh, Shamik Kundu

This paper introduces FlexNN, a Flexible Neural Network accelerator, which adopts agile design principles to enable versatile dataflows, enhancing energy efficiency.