Publications (8)
One Timestep is All You Need: Training Spiking Neural Networks with Ultra Low Latency
Sayeed Shafayet Chowdhury, Nitin Rathi, Kaushik Roy
Spiking Neural Networks (SNNs) are energy efficient alternatives to commonly used deep neural networks (DNNs). Through event-driven information processing, SNNs can reduce the expe…
Cache Bypassing and Checkpointing to Circumvent Data Security Attacks on STTRAM
Nitin Rathi, Asmit De, Helia Naeimi +1
Spin-Transfer Torque RAM (STTRAM) is promising for cache applications. However, it brings new data security issues that were absent in volatile memory counterparts such as Static R…
IMPULSE: A 65nm Digital Compute-in-Memory Macro with Fused Weights and Membrane Potential for Spike-based Sequential Learning Tasks
Amogh Agrawal, Mustafa Ali, Minsuk Koo +3
The inherent dynamics of the neuron membrane potential in Spiking Neural Networks (SNNs) allows processing of sequential learning tasks, avoiding the complexity of recurrent neural…
STDP Based Pruning of Connections and Weight Quantization in Spiking Neural Networks for Energy Efficient Recognition
Nitin Rathi, Priyadarshini Panda, Kaushik Roy
Spiking Neural Networks (SNNs) with a large number of weights and varied weight distribution can be difficult to implement in emerging in-memory computing hardware due to the limit…
Inherent Adversarial Robustness of Deep Spiking Neural Networks: Effects of Discrete Input Encoding and Non-Linear Activations
Saima Sharmin, Nitin Rathi, Priyadarshini Panda +1
In the recent quest for trustworthy neural networks, we present Spiking Neural Network (SNN) as a potential candidate for inherent robustness against adversarial attacks. In this w…
Side Channel Attacks on STTRAM and Low-Overhead Countermeasures
Nitin Rathi, Helia Naeimi, Swaroop Ghosh
Spin Transfer Torque RAM (STTRAM) is a promising candidate for Last Level Cache (LLC) due to high endurance, high density and low leakage. One of the major disadvantages of STTRAM…