7 papers
FrequencyFormer: A Co-Designed Sensor-to-Processor Pipeline for Frequency-Domain Vision Transformer Inference
Chengwei Zhou, Ovishake Sen, Xuming Chen +5
Deploying vision transformers (ViTs) on sensor-edge systems is limited not only by on-device compute, but also by the energy and bandwidth required to transmit high-dimensional ima…
Characterizing the Impact of NVFP4 Quantization for Low-Power Edge AI Deployment
Ovishake Sen, Venkata Nithin Kamineni, Daniel Lobo +3
Energy-efficient neural-network inference at the edge requires reducing arithmetic cost, memory traffic, computation energy, and storage overhead while maintaining acceptable accur…
Low-Latency Neural Inference on an Edge Device for Real-Time Handwriting Recognition from EEG Signals
Ovishake Sen, Raghav Soni, Darpan Virmani +6
Brain-computer interfaces (BCIs) offer a pathway to restore communication for individuals with severe motor or speech impairments. Imagined handwriting provides an intuitive paradi…
A Systematic Method for Optimum Biomedical Wireless Power Transfer using Inductive Links in Area-Constrained Implants
Asif Iftekhar Omi, Anyu Jiang, Baibhab Chatterjee
In the context of implantable bioelectronics, this work provides new insights into maximizing biomedical wireless power transfer (BWPT) via the systematic development of inductive…
Galvanic Body-Coupled Powering for Wireless Implanted Neurostimulators
Asif Iftekhar Omi, Emma Farina, Anyu Jiang +3
Body-coupled powering (BCP) is an innovative wireless power transfer (WPT) technique, recently explored for its potential to deliver power to cutting-edge biomedical implants such…
Look-Up Table based Neural Network Hardware
Ovishake Sen, Chukwufumnanya Ogbogu, Peyman Dehghanzadeh +4
Traditional digital implementations of neural accelerators are limited by high power and area overheads, while analog and non-CMOS implementations suffer from noise, device mismatc…
LUNA-CIM: Lookup Table based Programmable Neural Processing in Memory
Peyman Dehghanzadeh, Baibhab Chatterjee, Swarup Bhunia
This paper presents a novel approach for performing computations using Look-Up Tables (LUTs) tailored specifically for Compute-in-Memory applications. The aim is to address the sca…