70 citations · 99 across the 22 of their papers we have counts for
9 papers · 1 filter
TYTAN: Taylor-series based Non-Linear Activation Engine for Deep Learning Accelerators
Soham Pramanik, Vimal William, Arnab Raha +3
The rapid advancement in AI architectures and the proliferation of AI-enabled systems have intensified the need for domain-specific architectures that enhance both the acceleration…
COBRA: Catastrophic Bit-flip Reliability Analysis of State-Space Models
Sanjay Das, Swastik Bhattacharya, Shamik Kundu +3
State-space models (SSMs), exemplified by the Mamba architecture, have recently emerged as state-of-the-art sequence-modeling frameworks, offering linear-time scalability together…
SlimEdge: Performance and Device Aware Distributed DNN Deployment on Resource-Constrained Edge Hardware
Mahadev Sunil Kumar, Arnab Raha, Debayan Das +3
Distributed deep neural networks (DNNs) have become central to modern computer vision, yet their deployment on resource-constrained edge devices remains hindered by substantial par…
SafeCiM: Investigating Resilience of Hybrid Floating-Point Compute-in-Memory Deep Learning Accelerators
Swastik Bhattacharya, Sanjay Das, Anand Menon +3
Deep Neural Networks (DNNs) continue to grow in complexity with Large Language Models (LLMs) incorporating vast numbers of parameters. Handling these parameters efficiently in trad…
Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator
Akshat Ramachandran, Souvik Kundu, Arnab Raha +3
Large language model (LLM) pruning with fixed N:M structured sparsity significantly limits the expressivity of the sparse model, yielding sub-optimal performance. In contrast, supp…
XAMBA: Enabling Efficient State Space Models on Resource-Constrained Neural Processing Units
Arghadip Das, Arnab Raha, Shamik Kundu +3
State-Space Models (SSMs) have emerged as efficient alternatives to transformers for sequential data tasks, offering linear or near-linear scalability with sequence length, making…