4 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…
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…