The BondMachine, a moldable computer architecture

被引:3
|
作者
Mariotti, Mirko [1 ,2 ]
Magalotti, Daniel [2 ]
Spiga, Daniele [2 ]
Storchi, Loriano [2 ,3 ]
机构
[1] Univ Perugia, Dipartimento Fis & Geol, Via Pascoli, I-06123 Perugia, Italy
[2] Ist Nazl Fis Nucl, Sez Perugia, Via Pascoli, I-06123 Perugia, Italy
[3] Univ G DAnnunzio, Dipartimento Farm, I-66100 Chieti, Italy
关键词
Reconfigurable computing; BondMachine; Hardware; software co -design; Domain specific architectures; OPERATING SYSTEM; EFFICIENT;
D O I
10.1016/j.parco.2021.102873
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Future systems will be characterized by the presence of many computing core in a single device, on large scale data centers or even at the level of IoT devices. The ability to fully exploit computational architectures' heterogeneity and concurrency will be a key point. In this manuscript we present the BondMachine (BM), an innovative prototype software ecosystem aimed at creating facilities where hardware and software are co-designed, guaranteeing a full exploitation of fabric capabilities (both in terms of concurrency and heterogeneity) with several hardware optimization possibilities. The fundamental innovation of the BM is to provide a new kind of computer architecture, where the hardware dynamically adapts to the specific computational problem rather than being static and generic, as in standard CPUs synthesized in silicon. Hardware can be designed to fit precisely any computational task needs, implementing only the processing units needed and discarding generic solutions. By using BMs within FPGA technologies end-to-end solutions could be realized, in which the creation of domain-specific hardware is part of the development process as much as the software stack. FPGA technology allows to create independent processing units on a single low-power board, and to design their interconnections "in silicon"to maximally fit the design needs. The processors of the BMs are suitable for computational structures like neural networks and tensor processing models. Machine Learning (ML) and Deep Learning (DL) popularity keeps increasing in scientific and industrial areas.
引用
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页数:12
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