범용 신경망 연산기(ERNIE)를 위한 학습 모듈 설계

Design of Learning Module for ERNIE(ERNIE : Expansible & Reconfigurable Neuro Informatics Engine)

  • 정제교 (인하대 공대 정보통신공학과) ;
  • 위재우 (인하대 공대 전기공학과) ;
  • 동성수 (용인송담대 디지털전자정보과) ;
  • 이종호 (인하대 공대 정보통신공학과)
  • 발행 : 2004.12.01

초록

There are two important things for the general purpose neural network processor. The first is a capability to build various structures of neural network, and the second is to be able to support suitable learning method for that neural network. Some way to process various learning algorithms is required for on-chip learning, because the more neural network types are to be handled, the more learning methods need to be built into. In this paper, an improved hardware structure is proposed to compute various kinds of learning algorithms flexibly. The hardware structure is based on the existing modular neural network structure. It doesn't need to add a new circuit or a new program for the learning process. It is shown that rearrangements of the existing processing elements can produce several neural network learning modules. The performance and utilization of this module are analyzed by comparing with other neural network chips.

키워드