NDLI: The anytime automaton

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Volume 44, Issue 5, December 2016

Volume 44, Issue 4, September 2016

Volume 44, Issue 3, June 2016

Asymmetry-aware work-stealing runtimes

Back to the future

Automatic generation of efficient accelerators for reconfigurable hardware

Opportunistic competition overhead reduction for expediting critical section in NoC based CMPs

Base-victim compression

Decoupling loads for nano-instruction set computers

The anytime automaton

All-inclusive ECC

Using multiple input, multiple output formal control to maximize resource efficiency in architectures

Boosting access parallelism to PCM-based main memory

Efficient synonym filtering and scalable delayed translation for hybrid virtual caching

Energy efficient architecture for graph analytics accelerators

Transparent offloading and mapping (TOM)

Short-circuit dispatch

Bit-plane compression

Production-run software failure diagnosis via adaptive communication tracking

Future vector microprocessor extensions for data aggregations

Accelerating markov random field inference using molecular optical gibbs sampling units

Rescuing uncorrectable fault patterns in on-chip memories through error pattern transformation

Exploiting dynamic timing slack for energy efficiency in ultra-low-power embedded systems

Towards statistical guarantees in controlling quality tradeoffs for approximate acceleration

Efficient synonym filtering and scalable delayed translation for hybrid virtual caching

ARM virtualization

Efficiently scaling out-of-order cores for simultaneous multithreading

Peak efficiency aware scheduling for highly energy proportional servers

Evaluation of an analog accelerator for linear algebra

RelaxFault memory repair

Energy efficient data encoding in DRAM channels exploiting data value similarity

Accelerating dependent cache misses with an enhanced memory controller

Power attack defense

Volume 44, Issue 2, May 2016

Volume 44, Issue 1, May 2016

The anytime automaton

Content Provider	ACM Digital Library
Author	Jerger, Natalie Enright Miguel, Joshua San
Abstract	Approximate computing is an emerging paradigm enabling tradeoffs between accuracy and efficiency. However, a fundamental challenge persists: state-of-the-art techniques lack the ability to enforce runtime guarantees on accuracy. The convention is to 1) employ offline or online accuracy models, or 2) present experimental results that demonstrate empirically low error. Unfortunately, these approaches are still unable to guarantee acceptability of all application outputs at runtime. We offer a solution that revisits concepts from anytime algorithms. Originally explored for real-time decision problems, anytime algorithms have the property of producing results with increasing accuracy over time. We propose the Anytime Automaton, a new computation model that executes applications as a parallel pipeline of anytime approximations. An automaton produces approximate versions of the application output with increasing accuracy, guaranteeing that the final precise version is eventually reached. The automaton can be stopped whenever the output is deemed acceptable; otherwise, it is a simple matter of letting it run longer. We present an in-depth analysis of the model and demonstrate attractive runtime-accuracy profiles on various applications. Our anytime automaton is the first step towards systems where the acceptability of an application's output directly governs the amount of time and energy expended.
Starting Page	545
Ending Page	557
Page Count	13
File Format	PDF
ISSN	01635964
DOI	10.1145/3007787.3001195
Journal	ACM SIGARCH Computer Architecture News (CARN)
Volume Number	44
Issue Number	3
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	1981-04-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Content Type	Text
Resource Type	Article

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