3 citations · 3 across the 19 of their papers we have counts for
7 papers · 1 filter
HBQ: Hierarchical Scaling Block Quantization with Hardware-Efficiency-Aware Design for Accurate LLM Inference
Chun-Ting Chen, Dongmin Han, Hangyeol Mun +6
Block Quantization (BQ) is a promising approach for efficient deployment of large language models (LLMs), enabling low-precision computation with controlled accuracy degradation. C…
ReRAM-aware Model Finetuning addressing I-V Non-linearity and Retention Errors
Ching-Yi Lin, Shamik Kundu, Arnab Raha +1
Traditional CPU, GPU, and NPU architectures are increasingly limited by the von Neumann bottleneck. While In-Memory Computing (IMC) using ReRAM crossbar arrays offers a high-densit…
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…
Enhancing Large Language Models for Hardware Verification: A Novel SystemVerilog Assertion Dataset
Anand Menon, Samit S Miftah, Shamik Kundu +7
Hardware verification is crucial in modern SoC design, consuming around 70% of development time. SystemVerilog assertions ensure correct functionality. However, existing industrial…
GraNNite: Enabling High-Performance Execution of Graph Neural Networks on Resource-Constrained Neural Processing Units
Arghadip Das, Shamik Kundu, Arnab Raha +3
Graph Neural Networks (GNNs) are vital for learning from graph-structured data, enabling applications in network analysis, recommendation systems, and speech analytics. Deploying t…