Author: David Zhang
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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.
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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).
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Saccade Mechanisms for Image Classification, Object Detection and Tracking
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.
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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.
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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.
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Bit Efficient Quantization for Deep Neural Networks
In this paper, we present a comparison of model-parameter driven quantization approaches that can achieve as low as 3-bit precision without affecting accuracy.
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Fast, Full Chip Image Stitching of Nanoscale Integrated Circuits
In this paper, we describe the algorithmic steps taken in the processing pipeline to quickly create a global image database of an entire advanced IC.
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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.
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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.
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GPU Performance Prediction using Representation Learning
We propose a representation learning approach to address the high level of contention among thousands of parallel threads in GPU activity prediction models.
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Low Precision Neural Networks using Subband Decomposition
In this paper, we present a unique approach using lower precision weights for more efficient and faster training phase.
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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.