This work presents strategies to learn an Energy-Based Model (EBM) according to the desired length of its MCMC sampling trajectories.
To maintain real-time processing in embedded systems, large on-chip memory is required to buffer the interlayer feature maps.
The proposed computing engine is composed of a scalable CTT multiplier array and energy efficient analog-digital interfaces.
Deep convolutional neural networks (CNN) are widely used in modern artificial intelligence (AI) and smart vision systems but also limited by computation latency, throughput, and energy efficiency on a resource-limited scenario, such as mobile devices, internet of things (IoT), unmanned aerial vehicles (UAV), and so on.
To implement image detection using CNN in the internet of things (IoT) devices, a streaming hardware accelerator is proposed.