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Dynamically Specialized Datapaths for energy efficient computing
Abstract
Due to limits in technology scaling, energy efficiency of logic devices is decreasing in successive generations. To provide continued performance improvements without increasing power, regardless of the sequential or parallel nature of the application, microarchitectural energy efficiency must improve. We propose Dynamically Specialized Datapaths to improve the energy efficiency of general purpose programmable processors. The key insights of this work are the following. First, applications execute in phases and these phases can be determined by creating a path-tree of basic-blocks rooted at the inner-most loop. Second, specialized datapaths corresponding to these path-trees, which we refer to as DySER blocks, can be constructed by interconnecting a set of heterogeneous computation units with a circuit-switched network. These blocks can be easily integrated with a processor pipeline. A synthesized RTL implementation using an industry 55nm technology library shows a 64-functional-unit DySER block occupies approximately the same area as a 64 KB single-ported SRAM and can execute at 2 GHz. We extend the GCC compiler to identify path-trees and code-mapping to DySER and evaluate the PAR-SEC, SPEC and Parboil benchmarks suites. Our results show that in most cases two DySER blocks can achieve the same performance (within 5%) as having a specialized hardware module for each path-tree. A 64-FU DySER block can cover 12% to 100% of the dynamically executed instruction stream. When integrated with a dual-issue out-of-order processor, two DySER blocks provide geometric mean speedup of 2.1X (1.15X to 10X), and geometric mean energy reduction of 40% (up to 70%), and 60% energy reduction if no performance improvement is required.
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Cites
The PARSEC benchmark suite: characterization and architectural implications
Bienia, C., Kumar, S., Singh, J. P., & Li, K. (2008) · Proceedings of the 17th international conference on Parallel architectures and compilation techniques · doi:10.1145/1454115.1454128 · Cited by 3,444
Cited by
Eyeriss: a spatial architecture for energy-efficient dataflow for convolutional neural networks
Chen, Y.-H., Emer, J., & Sze, V. (2016) · ACM SIGARCH Computer Architecture News · doi:10.1145/3007787.3001177 · Cited by 1,182
From high-level deep neural models to FPGAs
Sharma, H., Park, J., Mahajan, D., Amaro, E., Kim, J. K., Shao, C., Mishra, A., & Esmaeilzadeh, H. (2016) · 2016 49th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1109/micro.2016.7783720 · Cited by 365
Is dark silicon useful?: harnessing the four horsemen of the coming dark silicon apocalypse
Taylor, M. B. (2012) · Proceedings of the 49th Annual Design Automation Conference · doi:10.1145/2228360.2228567 · Cited by 296
Metadata
- Authors
- Govindaraju, V., Ho, C.-H., & Sankaralingam, K.
- Venue
- 2011 IEEE 17th International Symposium on High Performance Computer Architecture
- Publisher
- IEEE
- Published
- 2011-02
- Pages
- 503-514
- ISSN
- 1530-0897 (ISSN-L)
- OpenAlex
- W2130408605
- Cited by
- 205
- References
- 1
- FWCI
- 15.29
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Govindaraju, V., Ho, C.-H., & Sankaralingam, K. (2011). Dynamically Specialized Datapaths for energy efficient computing. 2011 IEEE 17th International Symposium on High Performance Computer Architecture. https://doi.org/10.1109/hpca.2011.5749755
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