89 citations · 314 across the 33 of their papers we have counts for
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Real-Time Scheduling of Machine Learning Operations on Heterogeneous Neuromorphic SoC
Anup Das
Neuromorphic Systems-on-Chip (NSoCs) are becoming heterogeneous by integrating general-purpose processors (GPPs) and neural processing units (NPUs) on the same SoC. For embedded sy…
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…
Design-Technology Co-Optimization for NVM-based Neuromorphic Processing Elements
Shihao Song, Adarsha Balaji, Anup Das +1
Neuromorphic hardware platforms can significantly lower the energy overhead of a machine learning inference task. We present a design-technology tradeoff analysis to implement such…
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 (…
On the Mitigation of Read Disturbances in Neuromorphic Inference Hardware
Ankita Paul, Shihao Song, Twisha Titirsha +1
Non-Volatile Memory (NVM) cells are used in neuromorphic hardware to store model parameters, which are programmed as resistance states. NVMs suffer from the read disturb issue, whe…