most citedSpecial Session: Reliability Analysis for ML/AI Hardware

4 citations · 4 across the 3 of their papers we have counts for

collaborators

5 papers

cs.AR20214 cited

Special Session: Reliability Analysis for ML/AI Hardware

Shamik Kundu, Kanad Basu, Mehdi Sadi +4

Artificial intelligence (AI) and Machine Learning (ML) are becoming pervasive in today's applications, such as autonomous vehicles, healthcare, aerospace, cybersecurity, and many c…

cs.NE2021

On the Role of System Software in Energy Management of Neuromorphic Computing

Twisha Titirsha, Shihao Song, Adarsha Balaji +1

Neuromorphic computing systems such as DYNAPs and Loihi have recently been introduced to the computing community to improve performance and energy efficiency of machine learning pr…

cs.NE2021

Endurance-Aware Mapping of Spiking Neural Networks to Neuromorphic Hardware

Twisha Titirsha, Shihao Song, Anup Das +4

Neuromorphic computing systems are embracing memristors to implement high density and low power synaptic storage as crossbar arrays in hardware. These systems are energy efficient…

cs.NE2020

Thermal-Aware Compilation of Spiking Neural Networks to Neuromorphic Hardware

Twisha Titirsha, Anup Das

Hardware implementation of neuromorphic computing can significantly improve performance and energy efficiency of machine learning tasks implemented with spiking neural networks (SN…

cs.NE2020

Reliability-Performance Trade-offs in Neuromorphic Computing

Twisha Titirsha, Anup Das

Neuromorphic architectures built with Non-Volatile Memory (NVM) can significantly improve the energy efficiency of machine learning tasks designed with Spiking Neural Networks (SNN…