6 citations · 10 across the 6 of their papers we have counts for
7 papers
A Persistent-State Dataflow Accelerator for Memory-Bound Linear Attention Decode on FPGA
Neelesh Gupta, Peter Wang, Rajgopal Kannan +1
Gated DeltaNet (GDN) is a linear attention mechanism that replaces the growing KV cache with a fixed-size recurrent state. Hybrid LLMs like Qwen3-Next use 75% GDN layers and achiev…
Enabling Long FFT Convolutions on Memory-Constrained FPGAs via Chunking
Peter Wang, Neelesh Gupta, Viktor Prasanna
The need for long-context reasoning has led to alternative neural network architectures besides Transformers and self-attention, a popular model being Hyena, which employs causal 1…
Context-Driven Performance Modeling for Causal Inference Operators on Neural Processing Units
Neelesh Gupta, Rakshith Jayanth, Dhruv Parikh +1
The proliferation of large language models has driven demand for long-context inference on resource-constrained edge platforms. However, deploying these models on Neural Processing…
Benchmarking Edge AI Platforms for High-Performance ML Inference
Rakshith Jayanth, Neelesh Gupta, Viktor Prasanna
Edge computing's growing prominence, due to its ability to reduce communication latency and enable real-time processing, is promoting the rise of high-performance, heterogeneous Sy…
TabConv: Low-Computation CNN Inference via Table Lookups
Neelesh Gupta, Narayanan Kannan, Pengmiao Zhang +1
Convolutional Neural Networks (CNNs) have demonstrated remarkable ability throughout the field of computer vision. However, CNN inference requires a large number of arithmetic oper…
Attention, Distillation, and Tabularization: Towards Practical Neural Network-Based Prefetching
Pengmiao Zhang, Neelesh Gupta, Rajgopal Kannan +1
Attention-based Neural Networks (NN) have demonstrated their effectiveness in accurate memory access prediction, an essential step in data prefetching. However, the substantial com…
PaCKD: Pattern-Clustered Knowledge Distillation for Compressing Memory Access Prediction Models
Neelesh Gupta, Pengmiao Zhang, Rajgopal Kannan +1
Deep neural networks (DNNs) have proven to be effective models for accurate Memory Access Prediction (MAP), a critical task in mitigating memory latency through data prefetching. H…