DigitalPIM: Digital-based Processing In-Memory for Big Data Acceleration

Mohsen Imani, Saransh Gupta, Yeseong Kim, Minxuan Zhou, Tajana Rosing

Abstract: In this work, we design, DigitalPIM, a Digital-based Processing In-Memory platform capable of accelerating fundamental big data algorithms in real time with orders of magnitude more energy efficient operation. Unlike the existing near-data processing approach such as HMC 2.0, which utilizes additional low-power processing cores next to memory blocks, the proposed platform implements the entire algorithm directly in memory blocks without using extra processing units. In our platform, each memory block supports the essential operations including: bitwise operation, addition/multiplication, and search operation internally in memory without reading any values out of the block. This significantly mitigates the processing costs of the new architecture, while providing high scalability and parallelism for performing the extensive computations. We exploit these essential operations to accelerate popular big data applications entirely in memory such as machine learning algorithms, query processing, and graph processing. Our evaluations show that for all tested applications, the performance can be accelerated significantly by eliminating the memory access bottleneck