ARCHER: a ReRAM-based accelerator for compressed recommendation systems

doi:10.1007/s11704-023-3397-x

Frontiers of Computer Science

2024, Vol. 18

Issue (5): 185607 https://doi.org/10.1007/s11704-023-3397-x

本期目录

ARCHER: a ReRAM-based accelerator for compressed recommendation systems

Xinyang SHEN, Xiaofei LIAO, Long ZHENG(

), Yu HUANG, Dan CHEN, Hai JIN

National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Clusters and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China

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Abstract：

Modern recommendation systems are widely used in modern data centers. The random and sparse embedding lookup operations are the main performance bottleneck for processing recommendation systems on traditional platforms as they induce abundant data movements between computing units and memory. ReRAM-based processing-in-memory (PIM) can resolve this problem by processing embedding vectors where they are stored. However, the embedding table can easily exceed the capacity limit of a monolithic ReRAM-based PIM chip, which induces off-chip accesses that may offset the PIM profits. Therefore, we deploy the decomposed model on-chip and leverage the high computing efficiency of ReRAM to compensate for the decompression performance loss. In this paper, we propose ARCHER, a ReRAM-based PIM architecture that implements fully on-chip recommendations under resource constraints. First, we make a full analysis of the computation pattern and access pattern on the decomposed table. Based on the computation pattern, we unify the operations of each layer of the decomposed model in multiply-and-accumulate operations. Based on the access observation, we propose a hierarchical mapping schema and a specialized hardware design to maximize resource utilization. Under the unified computation and mapping strategy, we can coordinate the inter-processing elements pipeline. The evaluation shows that ARCHER outperforms the state-of-the-art GPU-based DLRM system, the state-of-the-art near-memory processing recommendation system RecNMP, and the ReRAM-based recommendation accelerator REREC by $15.79 ×$ , $2.21 ×$ , and $1.21 ×$ in terms of performance and $56.06 ×$ , $6.45 ×$ , and $1.71 ×$ in terms of energy savings, respectively.

Key words： recommendation system ReRAM processing-in-memory embedding layer

收稿日期: 2023-05-11 出版日期: 2023-12-15

Corresponding Author(s): Long ZHENG

引用本文:

. [J]. Frontiers of Computer Science, 2024, 18(5): 185607.
Xinyang SHEN, Xiaofei LIAO, Long ZHENG, Yu HUANG, Dan CHEN, Hai JIN. ARCHER: a ReRAM-based accelerator for compressed recommendation systems. Front. Comput. Sci., 2024, 18(5): 185607.

链接本文:

https://academic.hep.com.cn/fcs/CN/10.1007/s11704-023-3397-x
https://academic.hep.com.cn/fcs/CN/Y2024/V18/I5/185607

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

CPU	Intel Xeon CPU E5-2680 v4, 28 cores, 2.4 GHz
Cache	L1 64 KB, L2 256 KB, L3 35 MB
Main memory	256 GB DDR4

GPU	Tesla P100, 56 SMs $×$ 64 cores, 1.33 GHz
Cache	L1 64 KB/SM, L2 4 MB
GPU main memory	16 GB HBM2

RecNMP	4 channel $×$ 1 DIMM-NMP $×$ 2 Rank-NMPs
Memory	64 GB DRAM

REREC	419 MB MLP/Memory arrays, 1048 KB Inner-product arrays

Tab.1

Tab.2

Model	RMC0	RMC1	RMC2	RMC3	RMC4	RMC5	RMC6
Emb.	$1 ×$	$0.5 ×$	$2 ×$	$4 ×$	$1 ×$	$1 ×$	$1 ×$
MLP	$1 ×$	$1 ×$	$1 ×$	$1 ×$	$0.5 ×$	$2 ×$	$4 ×$
Size (GBs)	$2.1$	1.1	4.1	8.1	2.1	2.1	2.1

Tab.3

Component	Param.	Spec.	Pow. (mW)	Area (mm²)
ARU properties (8 ARUs per tile)
ADC	Number	32	64	0.00384
ADC	Resolution	8 b	64	0.00384
DAC	Number	32 $×$ 64	8	0.00034
DAC	Resolution	2 b	8	0.00034
S&H	Number	32 $×$ 64	0.020	0.000080
Crossbar	Number	32	6.2	0.0005
	Size	64 $×$ 64
	bits/cell	2
S&A	Number	16	0.80	0.00096
IR	Size	4 KB	2.32	0.0038
OR	Size	512 B	0.42	0.0014
Tile properties (64 PEs per chip)
ARU Total	Number	8	653.92	0.36384
Memory ReRAM	Size	2 MB	35.078	0.3440
EDRAM	Size	64 KB	20.7	0.083
Sigmoid	Number	2	0.52	0.0006
Chip properties
Tile Total	−	−	45.454K	50.652
NoC	Flit_size	128 b	75	0.58
Chip Total	−	−	45.529K	51.232

Tab.4

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