5 papers
Rethinking Vision Transformer Depth via Structural Reparameterization
Chengwei Zhou, Vipin Chaudhary, Gourav Datta
The computational overhead of Vision Transformers in practice stems fundamentally from their deep architectures, yet existing acceleration strategies have primarily targeted algori…
Learning Scalable Temporal Representations in Spiking Neural Networks Without Labels
Chengwei Zhou, Gourav Datta
Spiking neural networks (SNNs) exhibit temporal, sparse, and event-driven dynamics that make them appealing for efficient inference. However, extending these models to self-supervi…
BladderFormer: A Streaming Transformer for Real-Time Urological State Monitoring
Chengwei Zhou, Steve Majerus, Gourav Datta
Bladder pressure monitoring systems are increasingly vital in diagnosing and managing urinary tract dysfunction. Existing solutions rely heavily on hand-crafted features and shallo…
Opto-ViT: Architecting a Near-Sensor Region of Interest-Aware Vision Transformer Accelerator with Silicon Photonics
Mehrdad Morsali, Chengwei Zhou, Deniz Najafi +7
Vision Transformers (ViTs) have emerged as a powerful architecture for computer vision tasks due to their ability to model long-range dependencies and global contextual relationshi…
OASIS: Optimized Lightweight Autoencoder System for Distributed In-Sensor computing
Chengwei Zhou, Sreetama Sarkar, Yuming Li +2
In-sensor computing, which integrates computation directly within the sensor, has emerged as a promising paradigm for machine vision applications such as AR/VR and smart home syste…