Memory based digital signal processing

A cost-effective approach to increasing the processing throughput of many digital signal processing (DSP) systems is described.. This movement is achieved by migrating some of the basic computational elements to memory. The classic Von Neumann bottleneck is circumvented by localizing the computations to memory, and a high throughput is achieved by exploiting the memory architecture. For certain DSP applications that require a simple type of operation to be applied to a large amount of data, e.g. pattern recognition or matrix-matrix multiplications, the increase in throughput is very high. It is also shown that a system built with these memories is easier to use than systolic arrays or multiprocessor systems. As examples, compared to conventional solutions, a 10× improvement is shown for a continuous speech recognizer and a 16× improvement for a 128×128 matrix multiplication. Higher-orders-of-magnitude improvements are possible for larger problems and more memory chips