TY - GEN
T1 - Generalize or die
T2 - 2017 IEEE International Conference on Rebooting Computing, ICRC 2017
AU - Bruel, Pedro
AU - Chalamalasetti, Sai Rahul
AU - Dalton, Chris
AU - Hajj, Izzat El
AU - Goldman, Alfredo
AU - Graves, Catherine
AU - Hwu, Wen Mei
AU - Laplante, Phil
AU - Milojicic, Dejan
AU - Ndu, Geoffrey
AU - Strachan, John Paul
N1 - Publisher Copyright:
©2017 IEEE.
PY - 2017/11/28
Y1 - 2017/11/28
N2 - The deceleration of transistor feature size scaling has motivated growing adoption of specialized accelerators implemented as GPUs, FPGAS, ASICs, and more recently new types of computing such as neuromorphic, bio-inspired, ultra low energy, reversible, stochastic, optical, quantum, combinations, and others unforeseen. There is a tension between specialization and generalization, with the current state trending to master slave models where accelerators (slaves) are instructed by a general purpose system (master) running an Operating System (OS). Traditionally, an OS is a layer between hardware and applications and its primary function is to manage hardware resources and provide a common abstraction to applications. Does this function, however, apply to new types of computing paradigms? This paper revisits OS functionality for memristor-based accelerators. We explore one accelerator implementation, the Dot Product Engine (DPE), for a select pattern of applications in machine learning, imaging, and scientific computing and a small set of use cases. We explore typical OS functionality, such as reconfiguration, partitioning, security, virtualization, and programming. We also explore new types of functionality, such as precision and trustworthiness of reconfiguration. We claim that making an accelerator, such as the DPE, more general will result in broader adoption and better utilization.
AB - The deceleration of transistor feature size scaling has motivated growing adoption of specialized accelerators implemented as GPUs, FPGAS, ASICs, and more recently new types of computing such as neuromorphic, bio-inspired, ultra low energy, reversible, stochastic, optical, quantum, combinations, and others unforeseen. There is a tension between specialization and generalization, with the current state trending to master slave models where accelerators (slaves) are instructed by a general purpose system (master) running an Operating System (OS). Traditionally, an OS is a layer between hardware and applications and its primary function is to manage hardware resources and provide a common abstraction to applications. Does this function, however, apply to new types of computing paradigms? This paper revisits OS functionality for memristor-based accelerators. We explore one accelerator implementation, the Dot Product Engine (DPE), for a select pattern of applications in machine learning, imaging, and scientific computing and a small set of use cases. We explore typical OS functionality, such as reconfiguration, partitioning, security, virtualization, and programming. We also explore new types of functionality, such as precision and trustworthiness of reconfiguration. We claim that making an accelerator, such as the DPE, more general will result in broader adoption and better utilization.
UR - http://www.scopus.com/inward/record.url?scp=85043509311&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85043509311&partnerID=8YFLogxK
U2 - 10.1109/ICRC.2017.8123649
DO - 10.1109/ICRC.2017.8123649
M3 - Conference contribution
AN - SCOPUS:85043509311
T3 - 2017 IEEE International Conference on Rebooting Computing, ICRC 2017 - Proceedings
SP - 1
EP - 8
BT - 2017 IEEE International Conference on Rebooting Computing, ICRC 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 8 November 2017 through 9 November 2017
ER -