Vector Microprocessors by Krste Asanovic Doctor of Philosophy in Computer Science University of California, Berkeley Professor John Wawrzynek, Chair Most previous research into vector architectures has concentrated on supercomputing applications and small enhancements to existing vector supercomputer implementations. This thesis expands the body of vector research by examining designs appropriate for single-chip full-custom vector microprocessor implementations targeting a much broader range of applications. I present the design, implementation, and evaluation of T0 (Torrent-0): the first single-chip vector microprocessor. T0 is a compact but highly parallel processor that can sustain over 24 operations per cycle while issuing only a single 32-bit instruction per cycle. T0 demonstrates that vector architectures are well suited to full-custom VLSI implementation and that they perform well on many multimedia and human-machine interface tasks. The remainder of the thesis contains ...

Most Artificial Neural Networks (ANNs) have a fixed topology during learning, and typically suffer from a number of short-comings as a result. Variations of ANNs that use dynamic topologies have shown ability to overcome many of these problems. This paper introduces Location-Independent Transformations (LITs) as a general strategy for parallel implementation of feedforward networks that use dynamic topologies. A LIT creates a set of location-independent nodes, where each node computes its part of the network output independent of other nodes, using local information. This type of transformation allows efficient support for adding and deleting nodes dynamically during learning. This paper deals specifically with LITs for localist ANNs---localist in the sense that ultimately one node is responsible for each output. In particular, this paper presents LITs for two ANNs: a) the single-layer competitive learning network, and b) the counterpropagation network, which combines elements of supervised learning with competitive learning. The complexity of both learning and execution algorithms for both ANNs is linear in the number of inputs and logarithmic in the number of nodes in the original network.

Most Artificial Neural Networks (ANNs) have a fixed topology during learning, and often suffer from a number of short-comings as a result. Variations of ANNs that use dynamic topologies have shown ability to overcome many of these problems. This paper introduces Location-Independent Transformations (LITs) as a general strategy for implementing distributed feedforward networks that use dynamic topologies (dynamic ANNs) efficiently in parallel hardware. A LIT creates a set of location-independent nodes, where each node computes its part of the network output independent of other nodes, using local information. This type of transformation allows efficient support for adding and deleting nodes dynamically during learning. In particular, this paper presents an LIT for dynamic Backpropagation networks with a single hidden layer. The complexity of both learning and execution algorithms is O(n+p+logm) for a single pattern, where n is the number of inputs, p is the number of outputs, and m is the number of hidden nodes in the original network.

Most Artificial Neural Networks (ANNs) have a fixed topology during learning, and typically suffer from a number of short-comings as a result. Variations of ANNs that use dynamic topologies have shown ability to overcome many of these problems. This paper introduces Location-Independent Transformations (LITs) as a general strategy for implementing feedforward networks that use dynamic topologies. A LIT creates a set of location-independent nodes, where each node computes its part of the network output independent of other nodes, using local information. This type of transformation allows efficient support for adding and deleting nodes dynamically during learning. In particular, this paper presents LITs for the single-layer competitve learning network, and the counterpropagation network, which combines elements of supervised learning with competitive learning. These two networks are localist in the sense that ultimately one node is responsible for each output. LITs for other models are presented in other papers.

This paper introduces Location-Independent Transformations (LITs) as a general strategy for implementing neural networks that use static and dynamic topologies. A LIT creates a set of location-independent nodes, where each node computes its part of the network output independent of other nodes, using local information. This type of transformation allows efficient support for adding and deleting nodes dynamically during learning. Two simple networks, the single-layer competitive learning network, and the counterpropagation network, which combines elements of supervised learning with competitive learning, are used in this paper to illustrate the LIT strategy. These two networks are localist in the sense that ultimately one node is responsible for each output. LITs for other models are presented in other papers

Most Artificial Neural Networks (ANNs) have a fixed topology during learning, and often suffer from a number of short-comings as a result. Variations of ANNs that use dynamic topologies have shown ability to overcome many of these problems. This paper introduces Location-Independent Transformations (LITs) as a general strategy for implementing distributed feedforward networks that use dynamic topologies (dynamic ANNs) efficiently in parallel hardware. A LIT creates a set of location-independent nodes, where each node computes its part of the network output independent of other nodes, using local information. This type of transformation allows efficient support for adding and deleting nodes dynamically during learning. In particular, this paper presents an LIT for standard Backpropagation with two layers of weights, and shows how dynamic extensions to Backpropagation can be supported.

this paper with slightly different emphases also appear in Intelligent Systems [Rud95c], and the International Journal of Artificial Intelligence Tools [Rud95e]