56 citations · 147 across the 25 of their papers we have counts for
25 papers
DiffAxE: Diffusion-driven Hardware Accelerator Generation and Design Space Exploration
Arkapravo Ghosh, Abhishek Moitra, Abhiroop Bhattacharjee +2
Design space exploration (DSE) is critical for developing optimized hardware architectures, especially for AI workloads such as deep neural networks (DNNs) and large language model…
Low-power Spike-based Wearable Analytics on RRAM Crossbars
Abhiroop Bhattacharjee, Jinquan Shi, Wei-Chen Chen +2
This work introduces a spike-based wearable analytics system utilizing Spiking Neural Networks (SNNs) deployed on an In-memory Computing engine based on RRAM crossbars, which are k…
When In-memory Computing Meets Spiking Neural Networks -- A Perspective on Device-Circuit-System-and-Algorithm Co-design
Abhishek Moitra, Abhiroop Bhattacharjee, Yuhang Li +2
This review explores the intersection of bio-plausible artificial intelligence in the form of Spiking Neural Networks (SNNs) with the analog In-Memory Computing (IMC) domain, highl…
TReX- Reusing Vision Transformer's Attention for Efficient Xbar-based Computing
Abhishek Moitra, Abhiroop Bhattacharjee, Youngeun Kim +1
Due to the high computation overhead of Vision Transformers (ViTs), In-memory Computing architectures are being researched towards energy-efficient deployment in edge-computing sce…
PIVOT- Input-aware Path Selection for Energy-efficient ViT Inference
Abhishek Moitra, Abhiroop Bhattacharjee, Priyadarshini Panda
The attention module in vision transformers(ViTs) performs intricate spatial correlations, contributing significantly to accuracy and delay. It is thereby important to modulate the…
ClipFormer: Key-Value Clipping of Transformers on Memristive Crossbars for Write Noise Mitigation
Abhiroop Bhattacharjee, Abhishek Moitra, Priyadarshini Panda
Transformers have revolutionized various real-world applications from natural language processing to computer vision. However, traditional von-Neumann computing paradigm faces memo…
MCAIMem: a Mixed SRAM and eDRAM Cell for Area and Energy-efficient on-chip AI Memory
Duy-Thanh Nguyen, Abhiroop Bhattacharjee, Abhishek Moitra +1
AI chips commonly employ SRAM memory as buffers for their reliability and speed, which contribute to high performance. However, SRAM is expensive and demands significant area and e…
Are SNNs Truly Energy-efficient? A Hardware Perspective
Abhiroop Bhattacharjee, Ruokai Yin, Abhishek Moitra +1
Spiking Neural Networks (SNNs) have gained attention for their energy-efficient machine learning capabilities, utilizing bio-inspired activation functions and sparse binary spike-d…
RobustEdge: Low Power Adversarial Detection for Cloud-Edge Systems
Abhishek Moitra, Abhiroop Bhattacharjee, Youngeun Kim +1
In practical cloud-edge scenarios, where a resource constrained edge performs data acquisition and a cloud system (having sufficient resources) performs inference tasks with a deep…
HyDe: A Hybrid PCM/FeFET/SRAM Device-search for Optimizing Area and Energy-efficiencies in Analog IMC Platforms
Abhiroop Bhattacharjee, Abhishek Moitra, Priyadarshini Panda
Today, there are a plethora of In-Memory Computing (IMC) devices- SRAMs, PCMs & FeFETs, that emulate convolutions on crossbar-arrays with high throughput. Each IMC device offers it…
Examining the Role and Limits of Batchnorm Optimization to Mitigate Diverse Hardware-noise in In-memory Computing
Abhiroop Bhattacharjee, Abhishek Moitra, Youngeun Kim +2
In-Memory Computing (IMC) platforms such as analog crossbars are gaining focus as they facilitate the acceleration of low-precision Deep Neural Networks (DNNs) with high area- & co…
XPert: Peripheral Circuit & Neural Architecture Co-search for Area and Energy-efficient Xbar-based Computing
Abhishek Moitra, Abhiroop Bhattacharjee, Youngeun Kim +1
The hardware-efficiency and accuracy of Deep Neural Networks (DNNs) implemented on In-memory Computing (IMC) architectures primarily depend on the DNN architecture and the peripher…
DeepCAM: A Fully CAM-based Inference Accelerator with Variable Hash Lengths for Energy-efficient Deep Neural Networks
Duy-Thanh Nguyen, Abhiroop Bhattacharjee, Abhishek Moitra +1
With ever increasing depth and width in deep neural networks to achieve state-of-the-art performance, deep learning computation has significantly grown, and dot-products remain dom…
XploreNAS: Explore Adversarially Robust & Hardware-efficient Neural Architectures for Non-ideal Xbars
Abhiroop Bhattacharjee, Abhishek Moitra, Priyadarshini Panda
Compute In-Memory platforms such as memristive crossbars are gaining focus as they facilitate acceleration of Deep Neural Networks (DNNs) with high area and compute-efficiencies. H…
SpikeSim: An end-to-end Compute-in-Memory Hardware Evaluation Tool for Benchmarking Spiking Neural Networks
Abhishek Moitra, Abhiroop Bhattacharjee, Runcong Kuang +3
SNNs are an active research domain towards energy efficient machine intelligence. Compared to conventional ANNs, SNNs use temporal spike data and bio-plausible neuronal activation…
Examining the Robustness of Spiking Neural Networks on Non-ideal Memristive Crossbars
Abhiroop Bhattacharjee, Youngeun Kim, Abhishek Moitra +1
Spiking Neural Networks (SNNs) have recently emerged as the low-power alternative to Artificial Neural Networks (ANNs) owing to their asynchronous, sparse, and binary information p…
MIME: Adapting a Single Neural Network for Multi-task Inference with Memory-efficient Dynamic Pruning
Abhiroop Bhattacharjee, Yeshwanth Venkatesha, Abhishek Moitra +1
Recent years have seen a paradigm shift towards multi-task learning. This calls for memory and energy-efficient solutions for inference in a multi-task scenario. We propose an algo…
SATA: Sparsity-Aware Training Accelerator for Spiking Neural Networks
Ruokai Yin, Abhishek Moitra, Abhiroop Bhattacharjee +2
Spiking Neural Networks (SNNs) have gained huge attention as a potential energy-efficient alternative to conventional Artificial Neural Networks (ANNs) due to their inherent high-s…
Rate Coding or Direct Coding: Which One is Better for Accurate, Robust, and Energy-efficient Spiking Neural Networks?
Youngeun Kim, Hyoungseob Park, Abhishek Moitra +3
Recent Spiking Neural Networks (SNNs) works focus on an image classification task, therefore various coding techniques have been proposed to convert an image into temporal binary s…
Examining and Mitigating the Impact of Crossbar Non-idealities for Accurate Implementation of Sparse Deep Neural Networks
Abhiroop Bhattacharjee, Lakshya Bhatnagar, Priyadarshini Panda
Recently several structured pruning techniques have been introduced for energy-efficient implementation of Deep Neural Networks (DNNs) with lesser number of crossbars. Although, th…
Efficiency-driven Hardware Optimization for Adversarially Robust Neural Networks
Abhiroop Bhattacharjee, Abhishek Moitra, Priyadarshini Panda
With a growing need to enable intelligence in embedded devices in the Internet of Things (IoT) era, secure hardware implementation of Deep Neural Networks (DNNs) has become imperat…
Activation Density based Mixed-Precision Quantization for Energy Efficient Neural Networks
Karina Vasquez, Yeshwanth Venkatesha, Abhiroop Bhattacharjee +2
As neural networks gain widespread adoption in embedded devices, there is a need for model compression techniques to facilitate deployment in resource-constrained environments. Qua…
NEAT: Non-linearity Aware Training for Accurate and Energy-Efficient Implementation of Neural Networks on 1T-1R Memristive Crossbars
Abhiroop Bhattacharjee, Lakshya Bhatnagar, Youngeun Kim +1
Memristive crossbars suffer from non-idealities (such as, sneak paths) that degrade computational accuracy of the Deep Neural Networks (DNNs) mapped onto them. A 1T-1R synapse, add…
SwitchX: Gmin-Gmax Switching for Energy-Efficient and Robust Implementation of Binary Neural Networks on ReRAM Xbars
Abhiroop Bhattacharjee, Priyadarshini Panda
Memristive crossbars can efficiently implement Binarized Neural Networks (BNNs) wherein the weights are stored in high-resistance states (HRS) and low-resistance states (LRS) of th…