Titlebook: VLSI Design of Neural Networks; Ulrich Ramacher,Ulrich Rückert Book 1991 Springer Science+Business Media Dordrecht 1991 Processing.Signal.

核心

Asics for Prototyping of Pulse-Density Modulated Neural Networks,s fast prototyping of neural systems in a conventional digital microprocessor environment. It uses an ASIC cell library in combination with a Sea-Of-Gates template to produce testable integrated neural circuits with off-chip learning. Typical single-chip network sizes range from 18 neurons with 846

防銹

Evolve

Silicon Integration of Learning Algorithms and Other Auto-Adaptive Properties in a Digital Feedbacknetwork itself, leaving the burden of learning to a host, possibly parallel computer [3]. However, the idea of implementing training on the chip itself is attractive for two reasons: (i) the learning phase is usually very time-consuming; (ii) on-chip learning makes the network more autonomous and op

領(lǐng)帶

Fast Design of Digital Dedicated Neuro Chips, perform autonomously all the steps of the learning and the relaxation phases. Data circulation is implemented by shifting techniques. Customization of the network is done by setting identification data in dedicated memory elements. A neuron processor which performs the relaxation phase has been imp

Bouquet

collateral

Toroidal Neural Network: Architecture and Processor Granularity Issues,e fine-grained and richly-connected structure of neural networks means they map poorly onto the coarse-grained restricted IO bandwidth found in many MIMD architectures such as the transputer. This has stimulated a wide range of researchers to develop fine-grain parallel processing architectures capa

過(guò)于平凡

Unified Description of Neural Algorithms for Time-Independent Pattern Recognition, For the first time a unique set of 3 equations is derived which governs the learning dynamics of neural models that make use of objective functions. A general method to construct objective functions is outlined that helps organize the network output according to application-specific constraints. Se

Bricklayer

Design of a 1st Generation Neurocomputer,ls and, thus, make sense to be implemented in hardware. 2-D arrays composed of a specific VLSI Neural Signal Processor MA 16 that integrates these elementary strings as hard-wired functional blocks present a favourable solution to the architectural problem of mapping neural parallelity and adaptivit

小口啜飲

,From Hardware to Software: Designing a “Neurostation”,large number of elements, thus neural network architecture is linked to the concept of massive parallelism. Secondly, due to the iterative algorithms, a large number of processing steps is often necessary in order to ensure convergence and stability in the network. Therefore, hardware supporting the

Acetabulum