Troy A. Manning

Near-Data Processing: Insights from a MICRO-46 Workshop

The cost of data movement in big-data systems motivates careful examination of near-data processing (NDP) frameworks. The concept of NDP was actively researched in the 1990s, but gained little commercial traction. After a decade-long dormancy, interest in this topic has spiked. A workshop on NDP was organized at MICRO-46 and was well attended. Given the interest, the organizers and keynote speakers have attempted to capture the key insights from the workshop into an article that can be widely disseminated. This article describes the many reasons why NDP is compelling today and identifies key upcoming challenges in realizing the potential of NDP.

In-Memory Intelligence

Recent activity in near-data processing has built or proposed systems that can exploit technologies such as 3D stacks, in-situ computing, or dataflow devices. However, little effort has been applied to exploit the natural parallelism and throughput of DRAM. This article details research from Micron Technology in the area of processing in memory as a form of memory-centric computing. In-Memory Intelligence (IMI) attempts to place a massive array of bit-serial computing elements on pitch with the memory array, as close to the information as possible. This contrasts with near-memory devices that rely on some form of storage but must communicate with that storage via a fast, low-latency interface. Initial simulations and models show stair-step improvements in performance and power for various applications. Such technology allows DRAM to provide functionality in a heterogeneous system to alleviate the pressures of the von-Neumann barrier.