Computational sensing-low-power processing publications

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.

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

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).

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.

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

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.

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.

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.

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.