Understanding Bulk-Bitwise Processing In-Memory Through Database Analytics
Ben Perach, Ronny Ronen, Benny Kimelfeld, Shahar Kvatinsky

TL;DR
This paper explores bulk-bitwise processing-in-memory (PIM) for database analytics, demonstrating significant performance and energy efficiency improvements through a memristive system called PIMDB, evaluated via detailed simulations.
Contribution
It introduces PIMDB, a digital memristive PIM system for database analytics, with new techniques for in-memory filtering and aggregation, and a programming model supporting virtual memory.
Findings
Accelerates TPC-H queries by up to 608×
Reduces energy consumption by up to 18.6×
Maintains cell endurance within existing RRAM limits
Abstract
Bulk-bitwise processing-in-memory (PIM), where large bitwise operations are performed in parallel by the memory array itself, is an emerging form of computation with the potential to mitigate the memory wall problem. This paper examines the capabilities of bulk-bitwise PIM by constructing PIMDB, a fully-digital system based on memristive stateful logic, utilizing and focusing on in-memory bulk-bitwise operations, designed to accelerate a real-life workload: analytical processing of relational databases. We introduce a host processor programming model to support bulk-bitwise PIM in virtual memory, develop techniques to efficiently perform in-memory filtering and aggregation operations, and adapt the application data set into the memory. To understand bulk-bitwise PIM, we compare it to an equivalent in-memory database on the same host system. We show that bulk-bitwise PIM substantially…
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Taxonomy
TopicsAdvanced Memory and Neural Computing · Ferroelectric and Negative Capacitance Devices · Neuroscience and Neural Engineering
