MATHEMATICAL PROBLEM SOLVING CIRCUIT INCLUDING RESISTIVE ELEMENTS
Data di pubblicazione02-12-2019
Data di priorità01-01-1970
FaseItalian and PCT
TitolarePolitecnico di Milano
DipartimentoDepartment of Electronics, Information and Bioengineering
AutoriIelmini Daniele, Pedretti Giacomo, Sun Zhong
LOOPUS is a breakthrough in computing systems, enabling scalable, zero-latency, energy-efficient solution to machine learning and big data analytics via analogue in-memory accelerators. In particular it develops analogue accelerators for algebraic computing, delivering your machine learning and big data analytics in just one click.
Loopus is an innovative hardware accelerator for machine learning (ML) and processing of big data. It is developed a novel electronic circuit to train ML algorithms, including linear/logistic regression, neural network and page ranking in one step. The circuit is based on non-volatile analogue memories and feedback systems (thus the name Loopus).
It been developed the concept of loop computing for solving matrix equations and accelerating ML algorithms in one step, hence this technology speeds up the training phase for cloud and edge computing, thus saving time and cost for the data centres owners, and enabling low-power artificial intelligence (AI) processing at the edge.