Above are papers related to the following paper: [Paper_ID] Paper #739707. 	[Title] ce: [Tex89] <institution> Texas Instruments. </institution> <note> TMS320C30 C Compiler Reference Guide, 1989. Document Title: SPRU034A. </note> [Abstract] The design and implementation of software for the Ring Array Processor (RAP), a high performance parallel computer, involved development for three hardware platforms: Sun SPARC workstations, Heurikon MC68020 boards running the VxWorks real-time operating system, and Texas Instruments TMS320C30 DSPs. The RAP now runs in Sun workstations under UNIX and in a VME based system using VxWorks. A flexible set of tools has been provided both to the RAP user and programmer. Primary emphasis has been placed on improving the efficiency of layered artificial neural network algorithms. This was done by providing a library of assembly language routines, some of which use node-custom compilation. An object-oriented RAP interface in C++ is provided that allows programmers to incorporate the RAP as a computational server into their own UNIX applications. For those not wishing to program in C++, a command interpreter has been built that provides interactive and shell-script style RAP manipulation. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #162080. 	[Title] Connectionist Layered Object-Oriented Network Simulator (CLONES): User's Manual minimize the learning curve for using CLONES, [Abstract] CLONES is a object-oriented library for constructing, training and utilizing layered connectionist networks. The CLONES library contains all the object classes needed to write a simulator with a small amount of added source code (examples are included). The size of experimental ANN programs is greatly reduced by using an object-oriented library; at the same time these programs are easier to read, write and evolve. The library includes database, network behavior and training procedures that can be customized by the user. It is designed to run efficiently on data parallel computers (such as the RAP [6] and SPERT [1]) as well as uniprocessor workstations. While efficiency and portability to parallel computers are the primary goals, there are several secondary design goals: 3. allow heterogeneous algorithms and training procedures to be interconnected and trained together. Within these constraints we attempt to maximize the variety of artificial neural net work algorithms that can be supported. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #950305. 	[Title] Learning Topology-Preserving Maps Using Self-Supervised Backpropagation [Abstract] Self-supervised backpropagation is an unsupervised learning procedure for feedforward networks, where the desired output vector is identical with the input vector. For backpropagation, we are able to use powerful simulators running on parallel machines. Topology-preserving maps, on the other hand, can be developed by a variant of the competitive learning procedure. However, in a degenerate case, self-supervised backpropagation is a version of competitive learning. A simple extension of the cost function of backpropagation leads to a competitive version of self-supervised backpropagation, which can be used to produce topographic maps. We demonstrate the approach applied to the Traveling Salesman Problem (TSP). Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1109830. 	[Title] Simulation of Reduced Precision Arithmetic for Digital Neural Networks Using the RAP Machine [Abstract] This paper describes some of our recent work in the development of computer architectures for efficient execution of artificial neural network algorithms. Our earlier system, the Ring Array Processor (RAP), was a multiprocessor based on commercial DSPs with a low-latency ring interconnection scheme. We have used the RAP to simulate variable precision arithmetic and guide us in the design of higher performance neurocomputers based on custom VLSI. The RAP system played a critical role in this study, enabling us to experiment with much larger networks than would otherwise be possible. Our study shows that back-propagation training algorithms only require moderate precision. Specifically, 16b weight values and 8b output values are sufficient to achieve training and classification results comparable to 32b floating point. Although these results were gathered for frame classification in continuous speech, we expect that they will extend to many other connectionist calculations. We have used these results as part of the design of a programmable single chip microprocessor, SPERT. The reduced precision arithmetic permits the use of multiple units per processor. Also, reduced precision operands make more efficient use of valuable processor-memory bandwidth. For our moderate-precision fixed-point arithmetic applications, SPERT represents more than an order of magnitude reduction in cost over systems based on DSP chips. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1135345. 	[Title] A Symbolic Complexity Analysis of Connectionist Algorithms for Distributed-Memory Machines [Abstract] This paper attempts to rigorously determine the computation and communication requirements of connectionist algorithms running on a distributed-memory machine. The strategy involves (1) specifying key connectionist algorithms in a high-level object-oriented language, (2) extracting their running times as polynomials, and (3) analyzing these polynomials to determine the algorithms' space and time complexity. Results are presented for various implementations of the back-propagation algorithm [4]. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1365. 	[Title] Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex. [Abstract] Selective suppression of transmission at feedback synapses during learning is proposed as a mechanism for combining associative feedback with self-organization of feedforward synapses. Experimental data demonstrates cholinergic suppression of synaptic transmission in layer I (feedback synapses), and a lack of suppression in layer IV (feed-forward synapses). A network with this feature uses local rules to learn mappings which are not linearly separable. During learning, sensory stimuli and desired response are simultaneously presented as input. Feedforward connections form self-organized representations of input, while suppressed feedback connections learn the transpose of feedfor-ward connectivity. During recall, suppression is removed, sensory input activates the self-organized representation, and activity generates the learned response. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #162075. 	[Title] SPERT: A VLIW/SIMD Microprocessor for Artificial Neural Network Computations [Abstract] SPERT (Synthetic PERceptron Testbed) is a fully programmable single chip microprocessor designed for efficient execution of artificial neural network algorithms. The first implementation will be in a 1.2 m CMOS technology with a 50MHz clock rate, and a prototype system is being designed to occupy a double SBus slot within a Sun Sparcstation. SPERT will sustain over 300 fi 10 6 connections per second during pattern classification, and around 100 fi 10 6 connection updates per second while running the popular error backpropagation training algorithm. This represents a speedup of around two orders of magnitude over a Sparcstation-2 for algorithms of interest. An earlier system produced by our group, the Ring Array Processor (RAP), used commercial DSP chips. Compared with a RAP multiprocessor of similar performance, SPERT represents over an order of magnitude reduction in cost for problems where fixed-point arithmetic is satisfactory. fl International Computer Science Institute, 1947 Center Street, Berkeley, CA 94704 Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #738941. 	[Title] ICSIM: An Object-Oriented Connectionist Simulator gives an overview of the simulator. Its main concepts, the [Abstract] ICSIM is a connectionist net simulator being developed at ICSI and written in Sather. It is object-oriented to meet the requirements for flexibility and reuse of homogeneous and structured connectionist nets and to allow the user to encapsulate efficient customized implementations perhaps running on dedicated hardware. Nets are composed by combining off-the-shelf library classes and if necessary by specializing some of their behaviour. General user interface classes allow a uniform or customized graphic presentation of the nets being modeled. Please summarise all the given information.

Paper #739707 has the title `ce: [Tex89] <institution> Texas Instruments. </institution> <note> TMS320C30 C Compiler Reference Guide, 1989. Document Title: SPRU034A. </note>
` and the abstract `The design and implementation of software for the Ring Array Processor (RAP), a high performance parallel computer, involved development for three hardware platforms: Sun SPARC workstations, Heurikon MC68020 boards running the VxWorks real-time operating system, and Texas Instruments TMS320C30 DSPs. The RAP now runs in Sun workstations under UNIX and in a VME based system using VxWorks. A flexible set of tools has been provided both to the RAP user and programmer. Primary emphasis has been placed on improving the efficiency of layered artificial neural network algorithms. This was done by providing a library of assembly language routines, some of which use node-custom compilation. An object-oriented RAP interface in C++ is provided that allows programmers to incorporate the RAP as a computational server into their own UNIX applications. For those not wishing to program in C++, a command interpreter has been built that provides interactive and shell-script style RAP manipulation. 
`.
Question: Which category should Paper #739707 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #739707 has the title `ce: [Tex89] <institution> Texas Instruments. </institution> <note> TMS320C30 C Compiler Reference Guide, 1989. Document Title: SPRU034A. </note>
` and the abstract `The design and implementation of software for the Ring Array Processor (RAP), a high performance parallel computer, involved development for three hardware platforms: Sun SPARC workstations, Heurikon MC68020 boards running the VxWorks real-time operating system, and Texas Instruments TMS320C30 DSPs. The RAP now runs in Sun workstations under UNIX and in a VME based system using VxWorks. A flexible set of tools has been provided both to the RAP user and programmer. Primary emphasis has been placed on improving the efficiency of layered artificial neural network algorithms. This was done by providing a library of assembly language routines, some of which use node-custom compilation. An object-oriented RAP interface in C++ is provided that allows programmers to incorporate the RAP as a computational server into their own UNIX applications. For those not wishing to program in C++, a command interpreter has been built that provides interactive and shell-script style RAP manipulation. 
`.
Question: Which category should Paper #739707 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Neural_Networks