47 citations · 131 across the 18 of their papers we have counts for
21 papers
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…
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…
Design Technology Co-Optimization for Neuromorphic Computing
Ankita Paul, Shihao Song, Anup Das
We present a design-technology tradeoff analysis in implementing machine-learning inference on the processing cores of a Non-Volatile Memory (NVM)-based many-core neuromorphic hard…
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…
DFSynthesizer: Dataflow-based Synthesis of Spiking Neural Networks to Neuromorphic Hardware
Shihao Song, Harry Chong, Adarsha Balaji +3
Spiking Neural Networks (SNN) are an emerging computation model, which uses event-driven activation and bio-inspired learning algorithms. SNN-based machine-learning programs are ty…
Improving Inference Lifetime of Neuromorphic Systems via Intelligent Synapse Mapping
Shihao Song, Twisha Titirsha, Anup Das
Non-Volatile Memories (NVMs) such as Resistive RAM (RRAM) are used in neuromorphic systems to implement high-density and low-power analog synaptic weights. Unfortunately, an RRAM c…