4 citations · 5 across the 15 of their papers we have counts for
16 papers
Hardware-Aware Learned Representation Compression for Distributed In-Sensor Vision
Chengwei Zhou, Abu Masum, Xuming Chen +7
In-sensor computing reduces the cost of transmitting high-resolution image data by performing early-stage processing near the sensor. However, the logic chip integrated with a CMOS…
MOSAIC: Efficient Mixture-of-Agent Scheduling via Adaptive Aggregation and Inference Concurrency
Saptarshi Mitra, Yifan Zhang, Rachid Karami +5
Mixture-of-Agents (MoA) systems improve reasoning accuracy by routing each query to multiple expert LLMs and aggregating their outputs. Efficiently executing this workload on limit…
RedVTP: Training-Free Acceleration of Diffusion Vision-Language Models Inference via Masked Token-Guided Visual Token Pruning
Jingqi Xu, Jingxi Lu, Chenghao Li +3
Vision-Language Models (VLMs) have achieved remarkable progress in multimodal reasoning and generation, yet their high computational demands remain a major challenge. Diffusion Vis…
HIVTP: A Training-Free Method to Improve VLMs Efficiency via Hierarchical Visual Token Pruning Using Middle-Layer-Based Importance Score
Jingqi Xu, Jingxi Lu, Chenghao Li +2
Vision-Language Models (VLMs) have shown strong capabilities on diverse multimodal tasks. However, the large number of visual tokens output by the vision encoder severely hinders i…
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 +5
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…
Mitigating Hallucinations in Vision-Language Models through Image-Guided Head Suppression
Sreetama Sarkar, Yue Che, Alex Gavin +2
Despite their remarkable progress in multimodal understanding tasks, large vision language models (LVLMs) often suffer from "hallucinations", generating texts misaligned with the v…
Region Masking to Accelerate Video Processing on Neuromorphic Hardware
Sreetama Sarkar, Sumit Bam Shrestha, Yue Che +3
The rapidly growing demand for on-chip edge intelligence on resource-constrained devices has motivated approaches to reduce energy and latency of deep learning models. Spiking neur…
Energy-Efficient & Real-Time Computer Vision with Intelligent Skipping via Reconfigurable CMOS Image Sensors
Md Abdullah-Al Kaiser, Sreetama Sarkar, Peter A. Beerel +2
Current video-based computer vision (CV) applications typically suffer from high energy consumption due to reading and processing all pixels in a frame, regardless of their signifi…
MaskVD: Region Masking for Efficient Video Object Detection
Sreetama Sarkar, Gourav Datta, Souvik Kundu +3
Video tasks are compute-heavy and thus pose a challenge when deploying in real-time applications, particularly for tasks that require state-of-the-art Vision Transformers (ViTs). S…
Block Selective Reprogramming for On-device Training of Vision Transformers
Sreetama Sarkar, Souvik Kundu, Kai Zheng +1
The ubiquity of vision transformers (ViTs) for various edge applications, including personalized learning, has created the demand for on-device fine-tuning. However, training with…
Linearizing Models for Efficient yet Robust Private Inference
Sreetama Sarkar, Souvik Kundu, Peter A. Beerel
The growing concern about data privacy has led to the development of private inference (PI) frameworks in client-server applications which protects both data privacy and model IP.…
FixPix: Fixing Bad Pixels using Deep Learning
Sreetama Sarkar, Xinan Ye, Gourav Datta +1
Efficient and effective on-line detection and correction of bad-pixels can improve yield and increase the expected lifetime of image sensors. This paper presents a comprehensive De…
Technology-Circuit-Algorithm Tri-Design for Processing-in-Pixel-in-Memory (P2M)
Md Abdullah-Al Kaiser, Gourav Datta, Sreetama Sarkar +6
The massive amounts of data generated by camera sensors motivate data processing inside pixel arrays, i.e., at the extreme-edge. Several critical developments have fueled recent in…
Towards Power Efficient DNN Accelerator Design on Reconfigurable Platform
Rourab Paul, Sreetama Sarkar, Suman Sau +3
The exponential emergence of Field Programmable Gate Array (FPGA) has accelerated the research of hardware implementation of Deep Neural Network (DNN). Among all DNN processors, do…
Criticality Aware Soft Error Mitigation in the Configuration Memory of SRAM based FPGA
Swagata Mandal, Sreetama Sarkar, Wong Ming Ming +2
Efficient low complexity error correcting code(ECC) is considered as an effective technique for mitigation of multi-bit upset (MBU) in the configuration memory(CM)of static random…