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Improving Performance of Network-on-Memory Architectures via (De-)/Compression-in-DRAM
Conference proceeding   Open access

Improving Performance of Network-on-Memory Architectures via (De-)/Compression-in-DRAM

Arghavan Mohammadhassani and Anup Das
Proceedings of the 2023 ACM International Workshop on System-Level Interconnect Pathfinding, pp 1-10
15 Feb 2024
DOI: https://doi.org/10.1145/3632409.3640477
Featured in Collection :   Research Supported by Drexel Libraries' OA Programs
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https://doi.org/10.1145/3632409.3640477View
Published, Version of Record (VoR)Open Access via Drexel Libraries Read and Publish Program 2024CC BY V4.0,  Open

Abstract

Computer systems organization Computer systems organization -- Architectures Computer systems organization -- Architectures -- Parallel architectures Hardware Hardware -- Integrated circuits Hardware -- Integrated circuits -- Semiconductor memory
Network-on-chips (NoCs) are envisioned to be a scalable communication substrate for Network-on-Memory (NoM) architectures. However, modern data-intensive workloads continue to overwhelm the NoC link capacity, dramatically increasing memory service latency and causing a great performance loss. We introduce DECORAM, a data (de-)/compression scheme implemented within a DRAM-based NoM architecture. DECORAM uses a lookup table (LUT) to store compressed codes of common data patterns, and exploits this LUT during LLC misses to transmit these codes via NoC, instead of the original uncompressed data. We formulate compression and decompression mechanisms as a combination of LUT-based pattern matching and prefix concatenation, which are implemented using low-latency DRAM row activations and exploiting analog properties of the DRAM cell. To support DECORAM, we introduce a minimal design change of adding isolation transistors in a subarray to activate inter-subarray data movement based on the content of its row buffer. Our DECORAM controller reduces the compression and decompression latency by exploiting subarray-level parallelism to compress/decompress several CPU data misses, simultaneously. We evaluate DECORAM using data-intensive workloads from SPEC, APACHE, PARSEC, and in-memory computing benchmark suites. Our results show that compared to a baseline NoM, DECORAM significantly improves performance (average 30%) and energy (average 32%). Compared to a conventional NoC compression mechanism, DECORAM reduces memory area by 27% and energy by 12%, while delivering 7% higher performance improvement.

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