Hardware

Due to the compute intensive nature of machine learning algorithms, and in particular those that are analogue in nature, there has always been some demand for specific hardware to help accelerate the simulation software. This demand seems to have continuously competed with Moore’s Law and the constant improvement in general computation of personal computers and application servers. Specific utlizations include, GPUs and FPGAs, and dedicated hardware. Currently the focus seems to be around cloud providers delivering data analysis and machine learning platforms that anyone can leverage. See our seperate page on this.

• Deep Learning pushing GPU limits by theplatform.net
• Accelerating Deep Convolutional Neural Networks Using Specialized Hardware by Microsoft Research
• Deep Learning mentioned in Intel slides as promising application for altera FPGAs by forbes.com
• AI Supercomputer Built by Tapping Data Warehouses for Their Idle Computing Power by technologyreview.com
• NVIDIA.com Deep Learning GPU information plus alot of deep learning information
• BrainChip The Possibilities are Limitless
• Cirrascale Corporation is a premier developer of hardware and cloud-based solutions enabling GPU-driven deep learning infrastructure

Raspberry Pi and Python

• RPi and Python Projects in AI by rpiai.com
• RPi and Python Forums by raspberrypi.org

If you would like us to add a specific item to this list, please let us know via our add a link page

Also see our sub-categories

• Cloud Providers