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Embedded HPC


Embedded HPC


Embedded HPC and Embedded Deep Learning


Embedded and remote applications prohibit conventional HPC and deep learning solutions due to constraints on space (volume) and power consumption, and in some cases weight (collectively known as "SWaP", or size, weight, and power consumption). It's prohibitive to install a 300 W, 2-slot thick, 7 lb. GPU board (or equivalent Xeon Phi board) on a small form-factor motherboard such as pico-ITX, mini-ITX, PC104+, or similar.

For small Edge and IoT applications, a variety of small, low-power embedded targets have emerged, as listed below.

Embedded HPC and Embedded Deep Learning Targets

More info on the above HPC and deep learning embedded targets, including images and detailed descriptions, plus info on running compressed models on these targets including MobileNet and SqueezeNet, is on the SigDL Github page.

From pico-ITX and mini-ITX servers on up, coCPU accelerators can also solve these constraints, providing a dramatic increase in "HPC Density", expressed as Performance / Power / Volume / Weight. (GFlop/W/cm3/lb).

Embedded HPC and Embedded Deep Learning Applications

coCPU Accelerator Features

coCPU Accelerator Basic Specs

Embedded HPC Motherboard Features

Embedded HPC


Embedded HPC and Deep Learning Lab / Test Setup

Embedded HPC board boardtest


Embedded HPC board memtest


Embedded HPC board lab test