24 citations · 26 across the 5 of their papers we have counts for
5 papers
A Fully-Configurable Open-Source Software-Defined Digital Quantized Spiking Neural Core Architecture
Shadi Matinizadeh, Noah Pacik-Nelson, Ioannis Polykretis +9
We introduce QUANTISENC, a fully configurable open-source software-defined digital quantized spiking neural core architecture to advance research in neuromorphic computing. QUANTIS…
A Design Methodology for Fault-Tolerant Computing using Astrocyte Neural Networks
Murat Işık, Ankita Paul, M. Lakshmi Varshika +1
We propose a design methodology to facilitate fault tolerance of deep learning models. First, we implement a many-core fault-tolerant neuromorphic hardware design, where neuron and…
Implementing Spiking Neural Networks on Neuromorphic Architectures: A Review
Phu Khanh Huynh, M. Lakshmi Varshika, Ankita Paul +3
Recently, both industry and academia have proposed several different neuromorphic systems to execute machine learning applications that are designed using Spiking Neural Networks (…
Design of Many-Core Big Little μBrain for Energy-Efficient Embedded Neuromorphic Computing
M. Lakshmi Varshika, Adarsha Balaji, Federico Corradi +3
As spiking-based deep learning inference applications are increasing in embedded systems, these systems tend to integrate neuromorphic accelerators such as Brain to improve ener…
A Design Flow for Mapping Spiking Neural Networks to Many-Core Neuromorphic Hardware
Shihao Song, M. Lakshmi Varshika, Anup Das +1
The design of many-core neuromorphic hardware is getting more and more complex as these systems are expected to execute large machine learning models. To deal with the design compl…