Author: Michael Piacentino
-
Low-Power In-Pixel Computing with Current-Modulated Switched Capacitors
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.
-
Learning with Local Gradients at the Edge
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 Descent (tpSGD).
-
Real-Time Hyper-Dimensional Reconfiguration at the Edge using Hardware Accelerators
In this paper we present Hyper-Dimensional Reconfigurable Analytics at the Tactical Edge using low-SWaP embedded hardware that can perform real-time reconfiguration at the edge leveraging non-MAC deep neural nets (DNN) combined with hyperdimensional (HD) computing accelerators.
-
Model-Free Generative Replay For Lifelong Reinforcement Learning: Application To Starcraft-2
We evaluate our proposed algorithms on three different scenarios comprising tasks from the Starcraft 2 and Minigrid domains.
-
Hyper-Dimensional Analytics of Video Action at the Tactical Edge
We review HyDRATE, a low-SWaP reconfigurable neural network architecture developed under the DARPA AIE HyDDENN (Hyper-Dimensional Data Enabled Neural Network) program.
-
Automated Image Analysis and Classification Tool Based on Computer Vision Deep Learning Technologies
We present a rapid underwater video and automated image analysis tool using computer vision deep learning technologies.
-
Advances in Automated Stock Assessment Based on Computer Vision Deep Learning Technologies
We present a rapid fish assessment method leveraging computer vision deep learning technologies to provide both (1) rapid fish annotation and (2) fish classification with fish counting.
-
Unsupervised underwater fish detection fusing flow and objectiveness
In this paper, we propose a robust and unsupervised deep learning algorithm to automatically detect fish and thereby easing the burden of manual annotation.
-
Low-Light NV-CMOS Image Sensors for Day/Night Imaging
SRI’s new NV-CMOS™ image sensor technology is designed to capture images over the full range of illumination from bright sunlight to overcast starlight.
-
An Embedded Vision Services Framework for Heterogeneous Accelerators
This paper describes an architecture framework using heterogeneous hardware accelerators for embedded vision applications.
-
Motion Adaptive Signal Integration-High Dynamic Range (MASI-HDR) Video Processing for Dynamic Platforms
SRI’s MASI-HDR (Motion Adaptive Signal Integration-High Dynamic Range) is a novel technique for generating blur-reduced video using multiple captures for each displayed frame while increasing the effective camera dynamic range by four bits or more.
-
Extended Motion Adaptive Signal Integration Technique for Real-Time Image Enhancement
We propose a Motion Adaptive Signal Integration (MASI) algorithm that operates the sensor at a high frame rate, with real time alignment of individual image frames to form an enhanced quality video output.