Algo-Logic Systems Demonstrates Scale-Out Machine Learning and Real-time Inference Accelerated by FPGA Key-Value Store at SC17

San Jose, California, November 9, 2017 – Algo-Logic Systems demonstrated machine learning and real-time inference accelerated by their Key Value Store (KVS) running on a Field Programmable Gate Array (FPGA) at the Super Computing 2017 conference. The application to train and control self-driving cars was shown at the conference on November 13th through the 16th, 2017 in Denver, Colorado. In the demonstration, parallel simulations of cars were run and data was shared between computation nodes using Algo-Logic’s networked-attached KVS. Simulated sensor input and data from the KVS were used to control real-time inputs to self-driving cars.

The demonstration highlighted the capabilities of Algo-Logic’s hardware accelerated, in-memory KVS scale-out to support data center applications. Parallel simulations of a Markov model were run and data was shared between computation nodes using Algo-Logic’s networked-attached KVS that runs entirely in FPGA logic and interfaces through a software API to standard computation servers.

Accelerated processing teaches autonomous cars to drive in minutes

The Cube interview from SC17

Real-time Analytics & Scale-out Machine Learning with FPGA Key Value Store