PRINS: Processing-in-Storage Acceleration of Machine Learning

Machine learning algorithms have become a major tool in various applications. The high-performance requirements on large-scale datasets pose a challenge for traditional von Neumann architectures. We present two machine learning implementations and evaluations on PRINS, a novel processing-in-storage system based on resistive content addressable memory (ReCAM). PRINS functions simultaneously as a storage and a massively parallel associative processor. PRINS processing-in-storage resolves the bandwidth wall faced by near-data von Neumann architectures, such as three-dimensional DRAM and CPU stack or SSD with embedded CPU, by keeping the computing inside the storage arrays, thus implementing in-data, rather than near-data, processing. We show that PRINS-based processing-in-storage architecture may outperform existing in-storage designs and accelerator-based designs. Multiple performance comparisons for the ReCAM processing-in-storage implementations of $K$ -means and K-nearest neighbors are performed. Compared platforms include CPU, GPU, FPGA, and Automata Processor. We show that PRINS may achieve an order-of-magnitude speedup and improved power efficiency relative to all compared platforms.