Near-memory Computing on FPGAs with 3D-stacked Memories: Applications, Architectures, and Optimizations
Research output: Contribution to journal › Research article › Contributed › peer-review
Contributors
Abstract
The near-memory computing (NMC) paradigm has transpired as a promising method for overcoming the memory wall challenges of future computing architectures. Modern systems integrating 3D-stacked DRAM memory can be leveraged to prevent unnecessary data movement between the main memory and the CPU. FPGA vendors have started introducing 3D memories to their products in an effort to remain competitive on bandwidth requirements of modern memory-intensive applications. Recent NMC proposals target various types of data processing workloads such as graph processing, MapReduce, sorting, machine learning, and database analytics.In this article, we conduct a literature survey on previous proposals of NMC systems on FPGAs integrated with 3D memories. By leveraging the high bandwidth offered from such memories together with specifically designed hardware, FPGA architectures have become a competitor to GPU solutions in terms of speed and energy efficiency. Various FPGA-based NMC designs have been proposed with software and hardware optimization methods to achieve high performance and energy efficiency. Our review investigates various aspects of NMC designs such as platforms, architectures, workloads, and tools. We identify the key challenges and open issues with future research directions.
Details
Original language | English |
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Article number | 16 |
Journal | ACM Transactions on Reconfigurable Technology and Systems |
Volume | 16 |
Issue number | 1 |
Publication status | Published - 22 Dec 2022 |
Peer-reviewed | Yes |
External IDs
ORCID | /0000-0003-2571-8441/work/159607563 |
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Keywords
Sustainable Development Goals
ASJC Scopus subject areas
Keywords
- 3D stacking, FPGA architectures, high-bandwidth memory, Near-memory computing