18 papers
SpecReuse: Spectral Graph Reuse for Efficient Vision GNN Inference on FPGAs
Isabella Bernhardt Eiliya, Anvitha Ramachandran, Dhruv Parikh +1
Dynamic Image Graph Construction (DIGC) is the primary performance bottleneck in FPGA acceleration of Vision Graph Neural Networks (ViGs), reconstructing graph connectivity at ever…
FPGA Acceleration of Fully Homomorphic Encryption with Adaptive Key Switching
Zhihan Xu, Jayashree Adivarahan, Rajgopal Kannan +1
Fully Homomorphic Encryption (FHE) enables privacy-preserving cloud services but incurs substantial computation overhead, making hardware acceleration essential. Among FHE operatio…
FOLIO: Focused Semantic Memory for Streaming Video Understanding
Haoyang Fan, Dhruv Parikh, Anvitha Ramachandran +4
In online streaming video understanding, a video stream continues to arrive and queries may be issued at any time. Because streaming frames grow without bound, the system must cont…
Vision Non-Causal Trapezoidal Mamba: Eliminating Directional Scanning in Vision SSMs with Second-Order Dynamics
Anvitha Ramachandran, Dhruv Parikh, Haoyang Fan +2
State Space Models (SSMs) have emerged as an alternative to Vision Transformers, yet most vision SSMs inherit directional token scanning from causal sequence modeling. While effect…
GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA
Anvitha Ramachandran, Dhruv Parikh, Viktor Prasanna
Vision Graph Neural Networks (ViGs) represent an image as a graph of patch tokens, enabling adaptive, feature-driven neighborhoods. Unlike CNNs with fixed grid biases or Vision Tra…
ImageHD: Energy-Efficient On-Device Continual Learning of Visual Representations via Hyperdimensional Computing
Jebacyril Arockiaraj, Dhruv Parikh, Viktor Prasanna
On-device continual learning (CL) is critical for edge AI systems operating on non-stationary data streams, but most existing methods rely on backpropagation or exemplar-heavy clas…
Latent Denoising Improves Visual Alignment in Large Multimodal Models
Dhruv Parikh, Jacob Fein-Ashley, Rajgopal Kannan +1
Large Multimodal Models (LMMs) such as LLaVA are typically trained with an autoregressive language modeling objective, providing only indirect supervision to visual tokens. This of…
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…
ARMOR: Robust and Efficient CNN-Based SAR ATR through Model-Hardware Co-Design
Sachini Wickramasinghe, Tian Ye, Cauligi Raghavendra +1
Convolutional Neural Networks (CNNs) have achieved state-of-the-art accuracy in Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR). However, their high computational…
FAST-Prefill: FPGA Accelerated Sparse Attention for Long Context LLM Prefill
Rakshith Jayanth, Viktor Prasanna
In long-context large language model (LLM) inference, the prefill stage dominates computation due to self-attention over the complete input context. Sparse attention significantly…
Primitive-Driven Acceleration of Hyperdimensional Computing for Real-Time Image Classification
Dhruv Parikh, Jebacyril Arockiaraj, Viktor Prasanna
Hyperdimensional Computing (HDC) represents data using extremely high-dimensional, low-precision vectors, termed hypervectors (HVs), and performs learning and inference through lig…
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…
Efficient and Accurate Graph Classification with Hyperdimensional Computing on FPGA
Jebacyril Arockiaraj, Dhruv Parikh, Viktor Prasanna
Real-time, energy-efficient inference on edge devices is essential for graph classification across a range of applications. Hyperdimensional Computing (HDC) is a brain-inspired com…
Accelerating Dynamic Image Graph Construction on FPGA for Vision GNNs
Anvitha Ramachandran, Dhruv Parikh, Viktor Prasanna
Vision Graph Neural Networks (Vision GNNs, or ViGs) represent images as unstructured graphs, achieving state of the art performance in computer vision tasks such as image classific…
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…
ScalableHD: Scalable and High-Throughput Hyperdimensional Computing Inference on Multi-Core CPUs
Dhruv Parikh, Viktor Prasanna
Hyperdimensional Computing (HDC) is a brain-inspired computing paradigm that represents and manipulates information using high-dimensional vectors, called hypervectors (HV). Tradit…
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…
Benchmarking the Performance of Large Language Models on the Cerebras Wafer Scale Engine
Zuoning Zhang, Dhruv Parikh, Youning Zhang +1
Transformer based Large Language Models (LLMs) have recently reached state of the art performance in Natural Language Processing (NLP) and Computer Vision (CV) domains. LLMs use th…