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MATRIX: a reconfigurable computing architecture with configurable instruction distribution and deployable resources
Abstract
MATRIX is a novel, coarse-grain, reconfigurable computing architecture which supports configurable instruction distribution. Device resources are allocated to controlling and describing the computation on a per task basis. Application-specific regularity allows us to compress the resources allocated to instruction control and distribution, in many situations yielding more resources for datapaths and computations. The adaptability is made possible by a multi-level configuration scheme, a unified configurable network supporting both datapaths and instruction distribution, and a coarse-grained building block which can serve as an instruction store, a memory element, or a computational element. In a 0.5 /spl mu/ CMOS process, the 8-bit functional unit at the heart of the MATRIX architecture has a footprint of roughly 1.5 mm/spl times/1.2 mm, making single dies with over a hundred function units practical today. At this process point, 100 MHz operation is easily achievable, allowing MATRIX components to deliver on the order of 10 Gop/s (8-bit ops).
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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
The roles of FPGAs in reprogrammable systems
Hauck, S. (1998) · Proceedings of the IEEE · doi:10.1109/5.663540 · Cited by 373
Metadata
- Authors
- Mirsky, & DeHon
- Venue
- Proceedings IEEE Symposium on FPGAs for Custom Computing Machines FPGA-96
- Publisher
- IEEE
- Published
- 1996
- Pages
- 157-166
- OpenAlex
- W2122992089
- Cited by
- 281
- References
- 0
- FWCI
- 2.32
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Mirsky, & DeHon. (1996). MATRIX: a reconfigurable computing architecture with configurable instruction distribution and deployable resources. Proceedings IEEE Symposium on FPGAs for Custom Computing Machines FPGA-96. https://doi.org/10.1109/fpga.1996.564808
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