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Home » Publication » Computer vision publications » Computational sensing-low-power processing publications

Computational sensing-low-power processing publications

Computational sensing-low-power processing publications October 14, 2022

Low-Power In-Pixel Computing with Current-Modulated Switched Capacitors

David Zhang, Gooitzen van der Wal, Michael A. Isnardi, Michael Piacentino

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.

Computational sensing-low-power processing publications August 25, 2022

Sensor Trajectory Estimation by Triangulating Lidar Returns

Charles Karney

The paper describes how to recover the sensor trajectory for an aerial lidar collect using the data for multiple-return lidar pulses.

Computational sensing-low-power processing publications August 17, 2022

Learning with Local Gradients at the Edge

David Zhang, Michael Piacentino

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).

Computational sensing-low-power processing publications March 18, 2022

Real-Time Hyper-Dimensional Reconfiguration at the Edge using Hardware Accelerators

Gooitzen van der Wal, David Zhang, Michael Piacentino, Michael A. Isnardi

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.

Computational sensing-low-power processing publications April 1, 2021 Conference Paper

Hyper-Dimensional Analytics of Video Action at the Tactical Edge

Michael A. Isnardi, David Zhang, Michael Piacentino, Gooitzen van der Wal

We review HyDRATE, a low-SWaP reconfigurable neural network architecture developed under the DARPA AIE HyDDENN (Hyper-Dimensional Data Enabled Neural Network) program. 

Computational sensing-low-power processing publications December 16, 2019

Bit Efficient Quantization for Deep Neural Networks

David Zhang

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.

Computational sensing-low-power processing publications March 28, 2019

Fast, Full Chip Image Stitching of Nanoscale Integrated Circuits

David Zhang, Gooitzen van der Wal, David Stoker, David Weaver

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.

Computational sensing-low-power processing publications August 16, 2017

BitNet: Bit-Regularized Deep Neural Networks

SRI International

We present a novel optimization strategy for training neural networks which we call “BitNet”. Our key idea is to limit the expressive power of the network by dynamically controlling the range and set of values that the parameters can take.

Computational sensing-low-power processing publications April 1, 2015 Article

Enabling Smart Camera Networks with Smartphone Processors

SRI International

Distributed smart cameras exploit smartphone processor performance in their node communication and video metadata exchange, allowing the network to collectively reason in interpreting the scene, generating alerts, and making decisions.

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