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Engineering topics

Frenzel, J. F.

Publications and source records attributed to Frenzel, J. F..

Performance of defect-tolerant set-associative cache memories

The increased use of on-chip cache memories has led researchers to investigate their performance in the presence of manufacturing defects. Several techniques for yield improvement are discussed and results are presented which indicate that set-associativity may be used to provide defect tolerance as well as improve the cache performance. Tradeoffs between several cache organizations and replacement strategies are investigated and it is shown that token-based replacement may be a suitable alternative to the widely-used LRU strategy.

Frenzel, J. F.↗

Training product unit neural networks with genetic algorithms

The training of product neural networks using genetic algorithms is discussed. Two unusual neural network techniques are combined; product units are employed instead of the traditional summing units and genetic algorithms train the network rather than backpropagation. As an example, a neural netork is trained to calculate the optimum width of transistors in a CMOS switch. It is shown how local minima affect the performance of a genetic algorithm, and one method of overcoming this is presented.

Janson, D. J.↗

Supply Current Diagnosis in VLSI

This paper presents a technique based upon the power supply current signature (cd) which allows for the testing of mixed-signal systems, in situ. Through experiments with a microprocessor, the cd is shown to contain important information concerning the operational status of the system which may be easily extracted using approaches based on statistical signal detection theory. The fault-detection performance of these techniques is compared to that achieved through auto-regressive modeling of the cd.

Frenzel, J. F.↗