Open AccessDesigning high performance, power-efficient, reconfigurable compute structures for specialized applications
Author(s) -
Vladislav Shatravin,
Dmitriy Shashev
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1611/1/012071
Subject(s) - computer science , process (computing) , set (abstract data type) , homogeneous , computer architecture , artificial neural network , distributed computing , internet of things , power (physics) , embedded system , computer engineering , artificial intelligence , operating system , physics , quantum mechanics , thermodynamics , programming language
In this paper, a new approach to design high-performance and power-efficient computing structures are proposed for machine learning tasks. Such structures can be very useful in some specialized applications such as autonomous robots, mobile devices, smart sensors for the Internet of Things (IoT) and so on. This approach is based on the concept of reconfigurable homogeneous computing environments. Major advantages of this approach are discussed. The process of designing a set of elementary operations for such structures is described in detail using an example with a typical Feed-Forward Neural Network (FFNN) and its training module.