11 citations · 14 across the 6 of their papers we have counts for
6 papers
Efficient Model Adaptation for Continual Learning at the Edge
Zachary A. Daniels, Jun Hu, Michael Lomnitz +5
Most machine learning (ML) systems assume stationary and matching data distributions during training and deployment. This is often a false assumption. When ML models are deployed o…
Low-power In-pixel Computing with Current-modulated Switched Capacitors
David Zhang, Gooitzen van der Wal, Saurabh Farkya +4
We present a scalable in-pixel processing architecture that can reduce the data throughput by 10X and consume less than 30 mW per megapixel at the imager frontend. Unlike the state…
Learning with Local Gradients at the Edge
Michael Lomnitz, Zachary Daniels, David Zhang +1
To enable learning on edge devices with fast convergence and low memory, we present a novel backpropagation-free optimization algorithm dubbed Target Projection Stochastic Gradient…
Real-time Hyper-Dimensional Reconfiguration at the Edge using Hardware Accelerators
Indhumathi Kandaswamy, Saurabh Farkya, Zachary Daniels +8
In this paper we present Hyper-Dimensional Reconfigurable Analytics at the Tactical Edge (HyDRATE) using low-SWaP embedded hardware that can perform real-time reconfiguration at th…
Saccade Mechanisms for Image Classification, Object Detection and Tracking
Saurabh Farkya, Zachary Daniels, Aswin Nadamuni Raghavan +2
We examine how the saccade mechanism from biological vision can be used to make deep neural networks more efficient for classification and object detection problems. Our proposed a…
Bit Efficient Quantization for Deep Neural Networks
Prateeth Nayak, David Zhang, Sek Chai
Quantization for deep neural networks have afforded models for edge devices that use less on-board memory and enable efficient low-power inference. In this paper, we present a comp…