23 citations · 24 across the 8 of their papers we have counts for
8 papers
E-MagDiP: Electro-Magnetic based Differential Privacy for EEG based Community Sensing
Ayanga Imesha Kumari Kalupahana, Vishruti Ranjan, Li-Shiuan Peh
EEG-based community sensing programs are emerging globally as a tool to leverage aggregated brain data to gain insights into attentiveness of students and employees. But these prog…
COMPOSE: Static Timing-driven Composable Reconfigurable Architecture for Accelerating Recurrence-Bound Loops
Rohan Juneja, Vishruti Ranjan, Rakshith Harish +1
Coarse-Grained Reconfigurable Architectures (CGRAs) provide a spatially programmable substrate well suited for accelerating compute-intensive workloads with abundant parallelism. H…
Building an Open CGRA Ecosystem for Agile Innovation
Rohan Juneja, Pranav Dangi, Thilini Kaushalya Bandara +4
Modern computing workloads, particularly in AI and edge applications, demand hardware-software co-design to meet aggressive performance and energy targets. Such co-design benefits…
HALO: Hardware-aware quantization with low critical-path-delay weights for LLM acceleration
Rohan Juneja, Shivam Aggarwal, Safeen Huda +2
Quantization is critical for efficiently deploying large language models (LLMs). Yet conventional methods remain hardware-agnostic, limited to bit-width constraints, and do not acc…
Nexus Machine: An Active Message Inspired Reconfigurable Architecture for Irregular Workloads
Rohan Juneja, Pranav Dangi, Thilini Kaushalya Bandara +2
Modern reconfigurable architectures are increasingly favored for resource-constrained edge devices as they balance high performance, energy efficiency, and programmability well. Ho…
NOVA: NoC-based Vector Unit for Mapping Attention Layers on a CNN Accelerator
Mohit Upadhyay, Rohan Juneja, Weng-Fai Wong +1
Attention mechanisms are becoming increasingly popular, being used in neural network models in multiple domains such as natural language processing (NLP) and vision applications, e…
Shenjing: A low power reconfigurable neuromorphic accelerator with partial-sum and spike networks-on-chip
Bo Wang, Jun Zhou, Weng-Fai Wong +1
The next wave of on-device AI will likely require energy-efficient deep neural networks. Brain-inspired spiking neural networks (SNN) has been identified to be a promising candidat…
A Divide-and-Conquer Strategy for Parsing
Peh Li Shiuan, Christopher Ting Hian Ann
In this paper, we propose a novel strategy which is designed to enhance the accuracy of the parser by simplifying complex sentences before parsing. This approach involves the separ…