4 citations · 6 across the 7 of their papers we have counts for
4 papers · 1 filter
HALO: Memory-Centric Heterogeneous Accelerator with 2.5D Integration for Low-Batch LLM Inference
Shubham Negi, Kaushik Roy
The rapid adoption of Large Language Models (LLMs) has driven a growing demand for efficient inference, particularly in latency-sensitive applications such as chatbots and personal…
COMET: A Framework for Modeling Compound Operation Dataflows with Explicit Collectives
Shubham Negi, Manik Singhal, Aayush Ankit +2
Modern machine learning accelerators are designed to efficiently execute deep neural networks (DNNs) by optimizing data movement, memory hierarchy, and compute throughput. However,…
SpiDR: A Reconfigurable Digital Compute-in-Memory Spiking Neural Network Accelerator for Event-based Perception
Deepika Sharma, Shubham Negi, Trishit Dutta +2
Spiking Neural Networks (SNNs), with their inherent recurrence, offer an efficient method for processing the asynchronous temporal data generated by Dynamic Vision Sensors (DVS), m…
HCiM: ADC-Less Hybrid Analog-Digital Compute in Memory Accelerator for Deep Learning Workloads
Shubham Negi, Utkarsh Saxena, Deepika Sharma +1
Analog Compute-in-Memory (CiM) accelerators are increasingly recognized for their efficiency in accelerating Deep Neural Networks (DNN). However, their dependence on Analog-to-Digi…