23 citations · 98 across the 32 of their papers we have counts for
13 papers · 1 filter
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…
Input-Aware Dynamic Timestep Spiking Neural Networks for Efficient In-Memory Computing
Yuhang Li, Abhishek Moitra, Tamar Geller +1
Spiking Neural Networks (SNNs) have recently attracted widespread research interest as an efficient alternative to traditional Artificial Neural Networks (ANNs) because of their ca…
Sharing Leaky-Integrate-and-Fire Neurons for Memory-Efficient Spiking Neural Networks
Youngeun Kim, Yuhang Li, Abhishek Moitra +2
Spiking Neural Networks (SNNs) have gained increasing attention as energy-efficient neural networks owing to their binary and asynchronous computation. However, their non-linear ac…
Do We Really Need a Large Number of Visual Prompts?
Youngeun Kim, Yuhang Li, Abhishek Moitra +2
Due to increasing interest in adapting models on resource-constrained edges, parameter-efficient transfer learning has been widely explored. Among various methods, Visual Prompt Tu…
MINT: Multiplier-less INTeger Quantization for Energy Efficient Spiking Neural Networks
Ruokai Yin, Yuhang Li, Abhishek Moitra +1
We propose Multiplier-less INTeger (MINT) quantization, a uniform quantization scheme that efficiently compresses weights and membrane potentials in spiking neural networks (SNNs).…
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…
Workload-Balanced Pruning for Sparse Spiking Neural Networks
Ruokai Yin, Youngeun Kim, Yuhang Li +4
Pruning for Spiking Neural Networks (SNNs) has emerged as a fundamental methodology for deploying deep SNNs on resource-constrained edge devices. Though the existing pruning method…