A variety of computational tasks in early vision can be formulated through lattice networks. The cooperative action of these networks depends on the topology of interconnections, both feedforward and recurrent ones. This paper shows that it is possible to consider a distinct general architectural solution for all recurrent computations of any given order. The Gabor-like impulse response of a second-order network is analyzed in detail, pointing out how a near-optimal filtering behavior in space and frequency domains can be achieved through excitatory/inhibitory interactions without impairing the stability of the system. These architectures can be mapped, very efficiently at transistor level, on very large scale integration (VLSI) structures operating as analog perceptual engines. The problem of hardware implementation of early vision tasks can, indeed, be tackled by combining these perceptual agents through suitable weighted sums. A 17-node analog current-mode VLSI circuit has been implemented on a CMOS 2 microm, NWELL, single-poly, and double-metal technology, to demonstrate the feasibility of the approach. Applications of the perceptual engine to various machine vision algorithms are proposed.
We present and discuss the major results of our research activity aimed to the analog VLSI implementation of on-chip learning neural networks. In particular we present the SLANP (self learning neural processor) chip results. The SLANP architecture implements an on-chip learning multilayer perceptron network. The learning algorithm is based on the back propagation but it exhibits increased capabilities due to the local learning rate management. A prototype chip has been designed and fabricated in a CMOS 0.7 μm minimum channel length technology. The experimental results confirm the functionality of the chip and the soundness of the approach. The SLANP performance compares favorably with that reported in the literatur
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