The design, fabrication, and test of a new VLSI hybrid analog-digital neural processing element

A hybrid analog-digital neural processing element with the time-dependent behavior of biological neurons has been developed. The hybrid processing element is designed for VLSI implementation and offers the best attributes of both analog and digital computation. Custom VLSI layout reduces the layout area of the processing element, which in turn increases the expected network density. The hybrid processing element operates at the nanosecond time scale, which enables it to produce real-time solutions to complex spatiotemporal problems found in high-speed signal processing applications. VLSI prototype chips have been designed, fabricated, and tested with encouraging results. Systems utilizing the time-dependent behavior of the hybrid processing element have been simulated and are currently in the fabrication process. Future applications are also discussed.

[1]  J Wang,et al.  Coated amperometric electrode arrays for multicomponent analysis. , 1990, Analytical chemistry.

[2]  John H. R. Maunsell,et al.  Visual processing in monkey extrastriate cortex. , 1987, Annual review of neuroscience.

[3]  John Lazzaro,et al.  A Silicon Model Of Auditory Localization , 1989, Neural Computation.

[4]  Howard C. Card,et al.  Vlsi Devices and Circuits for Neural Networks , 1989, Int. J. Neural Syst..

[5]  J. Barnden,et al.  Winner-take-all networks: Time-based versus activation-based mechanisms for various selection goals , 1990, IEEE International Symposium on Circuits and Systems.

[6]  A. Hodgkin,et al.  Currents carried by sodium and potassium ions through the membrane of the giant axon of Loligo , 1952, The Journal of physiology.

[7]  Idan Segev,et al.  Compartmental models of complex neurons , 1989 .

[8]  Misha Mahowald,et al.  A silicon model of early visual processing , 1993, Neural Networks.

[9]  C. Miall,et al.  The diversity of neuronal properties , 1989 .

[10]  Y. Takefuji,et al.  Analog components for the VLSI of neural networks , 1990, IEEE Circuits and Devices Magazine.

[11]  D. C. Van Essen,et al.  Concurrent processing streams in monkey visual cortex , 1988, Trends in Neurosciences.

[12]  Alan F. Murray,et al.  Pulse-stream VLSI neural networks mixing analog and digital techniques , 1991, IEEE Trans. Neural Networks.

[13]  Jerome A. Feldman,et al.  Connectionist Models and Their Properties , 1982, Cogn. Sci..

[14]  Carver Mead,et al.  Analog VLSI and neural systems , 1989 .