Title :
Synapse-X: a general-purpose neurocomputer architecture
Author_Institution :
Siemens AG, Munich, Germany
Abstract :
A neurocomputer architecture is described which features the following characteristics: the compute-bound elementary operations are extracted from the set of neural algorithms; the elementary operations are executed by a specific VLSI neural signal processor MA16, and noncompute-bound operations by commercially available digital signal processors (DSPs) or microprocessors; the fine-grain systolic chip architecture is extensible to the board level as one- or two-dimensional systolic arrays of MA16s; each MA16 is provided with its own off-chip weight memory as well as template memory; it has systolic communication and control architecture for the array and the weight memory; it has VME and SBus interfaces; a neural algorithm programming language specifies the neural algorithms in terms of a sequence of elementary operations and their optional concatenations with DSP or host operations; and a cross-compiler translates nAPL into the machine language of the neurocomputer. The proposed architecture, SYNAPSE-1, can be considered a research instrument as well as a design platform for working out application-specific neural system architectures in terms of dedicated software and hardware
Keywords :
computer interfaces; digital signal processing chips; general purpose computers; neural nets; parallel algorithms; parallel languages; program compilers; systolic arrays; 2D systolic arrays; MA16; SBus interfaces; Synapse-X; VLSI neural signal processor; VME; cross-compiler; digital signal processors; fine-grain systolic chip architecture; general-purpose neurocomputer architecture; nAPL; neural algorithm programming language; template memory; weight memory; Communication system control; Computer architecture; Computer interfaces; Digital signal processing chips; Digital signal processors; Microprocessors; Signal processing; Signal processing algorithms; Systolic arrays; Very large scale integration;
Conference_Titel :
Neural Networks, 1991. 1991 IEEE International Joint Conference on
Print_ISBN :
0-7803-0227-3
DOI :
10.1109/IJCNN.1991.170709