Title
DRISA: a DRAM-based reconfigurable in-situ accelerator.
Abstract
Data movement between the processing units and the memory in traditional von Neumann architecture is creating the "memory wall" problem. To bridge the gap, two approaches, the memory-rich processor (more on-chip memory) and the compute-capable memory (processing-in-memory) have been studied. However, the first one has strong computing capability but limited memory capacity/bandwidth, whereas the second one is the exact the opposite. To address the challenge, we propose DRISA, a DRAM-based Reconfigurable In-Situ Accelerator architecture, to provide both powerful computing capability and large memory capacity/bandwidth. DRISA is primarily composed of DRAM memory arrays, in which every memory bitline can perform bitwise Boolean logic operations (such as NOR). DRISA can be reconfigured to compute various functions with the combination of the functionally complete Boolean logic operations and the proposed hierarchical internal data movement designs.We further optimize DRISA to achieve high performance by simultaneously activating multiple rows and subarrays to provide massive parallelism, unblocking the internal data movement bottlenecks, and optimizing activation latency and energy. We explore four design options and present a comprehensive case study to demonstrate significant acceleration of convolutional neural networks. The experimental results show that DRISA can achieve 8.8x speedup and 1.2x better energy efficiency compared with ASICs, and 7.7x speedup and 15x better energy efficiency over GPUs with integer operations.
Year
DOI
Venue
2017
10.1145/3123939.3123977
MICRO-50: The 50th Annual IEEE/ACM International Symposium on Microarchitecture Cambridge Massachusetts October, 2017
Keywords
Field
DocType
DRAM, Accelerator, Neural Network
Sense amplifier,Registered memory,Semiconductor memory,Interleaved memory,Computer science,Parallel computing,Computing with Memory,Real-time computing,Flat memory model,Computer memory,Memory controller
Conference
ISSN
ISBN
Citations 
1072-4451
978-1-4503-4952-9
57
PageRank 
References 
Authors
1.54
65
6
Name
Order
Citations
PageRank
Shuangchen Li163636.82
Dimin Niu260931.36
Krishna T. Malladi324918.37
Hongzhong Zheng41225.94
Bob Brennan5601.93
Yuan Xie66430407.00