Training a Limited-Interconnect, Synthetic Neural IC
Authors: S. Haghighi, A. Afghan, Larry A. Akers M. R. Walker
Published in: Advances in Neural Information Processing Systems 1  (NIPS 1988) (Main Conference Track), 1988

Abstract: Hardware implementation of neuromorphic algorithms is hampered by  high  degrees of connectivity.  Functionally equivalent feedforward  networks may be formed by using limited fan-in nodes and additional  layers.  but  this  complicates  procedures  for  determining  weight  magnitudes.  No direct mapping of weights exists between fully and  limited-interconnect  nets.  Low-level  nonlinearities  prevent  the  formation  of internal  representations  of widely  separated  spatial  features and the use of gradient descent methods to minimize output  error is hampered by error magnitude dissipation.  The judicious use  of linear summations or collection units is proposed as a solution. 
HARDWARE IMPLEMENTATIONS OF FEEDFORWARD, 
SYNTHETIC NEURAL SYSTEMS 
The pursuit of hardware implementations of artificial neural network models is motivated  by the need to develop  systems which are capable of executing neuromorphic algorithms  in  real  time.  The most significant barrier is  the  high  degree  of connectivity  required  between the processing elements.  Current interconnect technology does not support the  direct  implementation  of  large-scale  arrays  of  this  type.  In  particular.  the  high  fan-in/fan-outs  of biology  impose  connectivity  requirements  such  that  the  electronic  implementation  of a  highly  interconnected biological  neural  networks  of just a  few  thousand neurons would require a level of connectivity which exceeds the current or even  projected interconnection density ofULSI systems (Akers et al.  1988). 
Highly layered. limited-interconnected architectures are however. especially well suited for  VLSI  implementations.  In  previous  works.  we  analyzed  the  generalization  and  fault-tolerance characteristics of a limited-interconnect perceptron architecture applied in  three simple mappings between binary input space and binary output space and proposed a  CMOS architecture (Akers and Walker. 1988).  This paper concentrates on developing an  understanding  of the limitations on  layered  neural  network  architectures  imposed by  hardware implementation and a proposed solution. 
778 
Walker, Haghighi, Afghan and Akers 
TRAINING CONSIDERATIONS FOR 
LIMITED .. INTERCONNECT FEEDFORWARD NETWORKS 
The symbolic layout of the limited fan-in  network is shown in Fig.  1.  Re-arranging of  the individual input components is done to eliminate edge effects.  Greater detail on the  actual  hardware architecture may be found  in  (Akers and Walker,  1988)  As  in  linear  filters,  the  total  number  of connections  which  fan-in  to  a  given  processing  element  determines the degrees of freedom available for forming a hypersurface which implements  the desired node output function (Widrow and Stearns, 1985).  When processing elements  with fixed, low fan-in are employed, the affects of reduced  degrees of freedom  must be  considered in order to develop workable training methods which permit generalization of  novel  inputs.  First. no  direct or indirect relation  exists  between  weight  magnitudes  obtained for a limited-interconnect, multilayered perceptron, and those obtained for  the  fully connected case.  Networks of these types adapted with identical exemplar sets must  therefore fonn  completely different functions  on  the input  space.  Second,  low-level  nonlinearities prevent direct internal  coding of widely  separated spatial features  in  the  input set.  A related problem  arises  when hyperplane nonlinearities are used.  Multiple  hyperplanes required on a subset of input space are impossible when no two second level  nodes  address  identical positions  in  the  input  space.  Finally,  adaptation  methods  like  backpropagation which minimize output error with gradient descent are hindered since the  magnitude of the error is dissipated as it back-propagates through large numbers of hidden  layers.  The appropriate placement of linear summation elements or collection  units is a  proposed solution.