A Design Methodology for Fault-Tolerant Computing using Astrocyte Neural Networks

no code implementations • 6 Apr 2022 • Murat Işık, Ankita Paul, M. Lakshmi Varshika, Anup Das

We propose a design methodology to facilitate fault tolerance of deep learning models.

Implementing Spiking Neural Networks on Neuromorphic Architectures: A Review

no code implementations • 17 Feb 2022 • Phu Khanh Huynh, M. Lakshmi Varshika, Ankita Paul, Murat Isik, Adarsha Balaji, Anup Das

Here, we provide a comprehensive overview of such frameworks proposed for both, platform-based design and hardware-software co-design.

BIG-bench Machine Learning

Design of Many-Core Big Little μBrain for Energy-Efficient Embedded Neuromorphic Computing

no code implementations • 23 Nov 2021 • M. Lakshmi Varshika, Adarsha Balaji, Federico Corradi, Anup Das, Jan Stuijt, Francky Catthoor

We propose a system software framework called SentryOS to map SDCNN inference applications to the proposed design.

A Design Flow for Mapping Spiking Neural Networks to Many-Core Neuromorphic Hardware

no code implementations • 27 Aug 2021 • Shihao Song, M. Lakshmi Varshika, Anup Das, Nagarajan Kandasamy

We propose an SDFG-based design flow for mapping spiking neural networks (SNNs) to many-core neuromorphic hardware with the objective of exploring the tradeoff between throughput and buffer size.

graph partitioning