Massively Parallel Big Data Classification on a Programmable Processing In-Memory Architecture

With the emergence of Internet of Things, massive data created in the world pose huge technical challenges for efficient processing. Processing in-memory (PIM) technology has been widely investigated to overcome expensive data movements between processors and memory bloclcs. However, existing PIM designs incur large area overhead to enable computing capability via additional near-data processing cores and analog/mixed signal circuits. In this paper, we propose a new massively-parallel processing in-memory (PIM) architecture, called CHOIR, based on emerging nonvolatile memory technology for big data classification. Unlike existii PIM designs which demand large analog/mixed signal circuits, we support the parallel PIM instructions for conditional and arithmetic operations in an area-efficient way. As a result, the classification solution performs both training and testing on the PIM architecture by fully utilizing the massive parallelism. Our design significantly improves the performance and energy áfidency of the classification tasks by 123 x and 52 x respectively as compared to the state-of-the-art tree boosting library running on GPU.