This paper describes the implementation of a fast neural net simulator on a novel parallel distributed-memory computer. A 60-processor system, named MUSIC (multiprocessor system with intelligent communication), is operational and runs the backpropagation algorithm at a speed of 330 million connection updates per second (continuous weight update) using 32-b floating-point precision. This is equal to 1.4 Gflops sustained performance. The complete system with 3.8 Gflops peak performance consumes less than 800 W of electrical power and fits into a 19-in rack. While reaching the speed of modern supercomputers, MUSIC still can be used as a personal desktop computer at a researcher's own disposal. In neural net simulation, this gives a computing performance to a single user which was unthinkable before. The system's real-time interfaces make it especially useful for embedded applications.

This paper compares the performance of several classifier algorithms on a standard database of handwritten digits. We consider not only raw accuracy, but also training time, recognition time, and memory requirements. When available, we report measurements of the fraction of patterns that must be rejected so that the remaining patterns have misclassification rates less than a given threshold.

This paper describes a parallel distributed computer architecture called MUSIC (multi signal processor system with intelligent communication). A single processor element (PE) consists of a DSP 96002 from Motorola (60 MFlops), program and data memory and a fast, independent communication interface; all communication interfaces are connected through a communication ring. A system with 30 processor elements (PEs) is operational. It has a peak performance of 1.8 GFlops, an electrical power consumption of about 350 watt (including forced air cooling). It fits into a 19 inch rack. The hardware price of this system is 40000 US $ which means a selling price of approximately 200000 US $. Beside the wellknown Mandelbrot algorithm (601 MFlops sustained), two real applications are at the moment successfully implemented on the system: the backpropagation algorithm for neural net learning results in a peak performance of 150 MCUPS (million connection updates per second) which equals 900 MFlops sustained and the molecular dynamics simulation program MD-Atom (443 MFlops sustained). Other applications of the system are in digital signal processing and finite element computation.>

The authors discuss the MUSIC (multi-signal-processor system with intelligent communication) system, a parallel distributed memory architecture based on digital signal processors (DSPs). A system with 60 processor elements is operational. It has a peak performance of 3.8 GFlops and an electrical power consumption of less than 800 W (including forced air cooling), and it fits into a 19" rack. Two applications (the backpropagation algorithm for neural network learning and molecular dynamics simulations) run about six times faster than on a CRAY Y-MP and two times faster than on a NEC SX-3. A sustained performance of more than 1 GFlops is reached.>