A board is described that contains the ANN A neural-network chip, and a DSP32C digital signal processor. The ANNA (Analog Neural Network Arithmetic unit) chip performs mixed analog/digital processing. The combination of ANNA with the DSP allows high-speed, end-to-end ex(cid:173) ecution of numerous signal-processing applications, including the prepro(cid:173) cessing, the neural-net calculations, and the postprocessing steps. The ANNA board evaluates neural networks 10 to 100 times faster than the DSP alone. The board is suitable for implementing large (million con(cid:173) nections) networks with sparse weight matrices.

A board is described that contains the ANNA neural-network chip, and a DSP32C digital signal processor. The ANNA (Analog Neural Network Arithmetic unit) chip performs mixed analog/digital processing. The combination of ANNA with the DSP allows high-speed, end-to-end execution of numerous signal-processing applications, including the preprocessing, the neural-net calculations, and the postprocessing steps. The ANNA board evaluates neural networks 10 to 100 times faster than the DSP alone. The board is suitable for implementing large (million connections) networks with sparse weight matrices. Three applications have been implemented on the board: a convolver network for slant detection of text blocks, a handwritten digit recognizer, and a neural network for recognition-based segmentation.

Many researchers have built neural-network chips, but few chips have been installed in board-level systems, even though this next level of integration provides insights and advantages that can't be attained on a chip testing station. Building a board demonstrates whether or not the chip can be effectively integrated into the larger systems required for real applications.

Many researchers have built neural-network chips, but few chips have been installed in board-level systems, even though this next level of integration provides insights and advantages that can't be attained on a chip testing station. Building a board demonstrates whether or not the chip can be effectively integrated into the larger systems required for real applications.

Many researchers have built neural-network chips, but few chips have been installed in board-level systems, even though this next level of integration provides insights and advantages that can't be attained on a chip testing station. Building a board demonstrates whether or not the chip can be effectively integrated into the larger systems required for real applications.