15 citations · 15 across the 6 of their papers we have counts for
6 papers
SPARQLe: Sub-Precision Activation Representation for Quantized LLM Inference
Aradhana Mohan Parvathy, Soumendu Kumar Ghosh, Shamik Kundu +4
The rapid growth in sizes of Large language models (LLMs) results in high compute and memory costs during inference. Quantization has been a significant pathway to addressing this…
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…
StruM: Structured Mixed Precision for Efficient Deep Learning Hardware Codesign
Michael Wu, Arnab Raha, Deepak A. Mathaikutty +3
In this paper, we propose StruM, a novel structured mixed-precision-based deep learning inference method, co-designed with its associated hardware accelerator (DPU), to address the…
FlexNN: A Dataflow-aware Flexible Deep Learning Accelerator for Energy-Efficient Edge Devices
Arnab Raha, Deepak A. Mathaikutty, Soumendu K. Ghosh +1
This paper introduces FlexNN, a Flexible Neural Network accelerator, which adopts agile design principles to enable versatile dataflows, enhancing energy efficiency. Unlike convent…