2017 IEEE International Conference on Rebooting Computing (ICRC)最新文献

Generalize or Die: Operating Systems Support for Memristor-Based Accelerators 泛化或死亡:操作系统支持基于忆阻器的加速器

2017 IEEE International Conference on Rebooting Computing (ICRC) Pub Date : 2017-11-28 DOI: 10.1109/ICRC.2017.8123649

P. Bruel, S. R. Chalamalasetti, Chris I. Dalton, I. E. Hajj, A. Goldman, Catherine E. Graves, Wen-mei W. Hwu, Phil Laplante, D. Milojicic, Geoffrey Ndu, J. Strachan

{"title":"Generalize or Die: Operating Systems Support for Memristor-Based Accelerators","authors":"P. Bruel, S. R. Chalamalasetti, Chris I. Dalton, I. E. Hajj, A. Goldman, Catherine E. Graves, Wen-mei W. Hwu, Phil Laplante, D. Milojicic, Geoffrey Ndu, J. Strachan","doi":"10.1109/ICRC.2017.8123649","DOIUrl":"https://doi.org/10.1109/ICRC.2017.8123649","url":null,"abstract":"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.","PeriodicalId":125114,"journal":{"name":"2017 IEEE International Conference on Rebooting Computing (ICRC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123434510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

Reducing Binary Quadratic Forms for More Scalable Quantum Annealing 简化二元二次型的更可伸缩量子退火

2017 IEEE International Conference on Rebooting Computing (ICRC) Pub Date : 2017-11-01 DOI: 10.1109/ICRC.2017.8123654

Georg Hahn, H. Djidjev

引用次数: 11

Reconfigurable and Programmable Ion Trap Quantum Computer 可重构可编程离子阱量子计算机

2017 IEEE International Conference on Rebooting Computing (ICRC) Pub Date : 2017-11-01 DOI: 10.1109/ICRC.2017.8123665

Stewart Allen, Jungsang Kim, D. Moehring, C. Monroe

引用次数: 6

Auditory Neural Pathway Simulation 听觉神经通路模拟

2017 IEEE International Conference on Rebooting Computing (ICRC) Pub Date : 2017-11-01 DOI: 10.1109/ICRC.2017.8123633

R. Meeson

{"title":"Auditory Neural Pathway Simulation","authors":"R. Meeson","doi":"10.1109/ICRC.2017.8123633","DOIUrl":"https://doi.org/10.1109/ICRC.2017.8123633","url":null,"abstract":"We describe an effort to simulate the neural pathway from the inner ear (cochlea) to the primary auditory cortex in the brain. The human cochlea contains sensory cells (inner hair cells), which respond to the mechanical motion of traveling waves that sweep along the basilar membrane. Neurons triggered by the sensory cells carry sound signals from the cochlea to the brain through a series of a half-dozen transfer sites. At each junction, firing neurons stimulate some and inhibit other neighboring neurons. The signal processing effects of these interactions are not fully understood. The net behavior is difficult to observe in-vivo because the neurons are not easily accessible and only a relatively few can be measured at one time. As a result, the \"neural code\" that represents sound signals is not understood. We do know, however, that our perception of sound is much more refined than the signal observable at the cochlea. Frequencies are only broadly separated within the cochlea, for example, yet we are able to perceive very narrow differences in pitch. The simulation model we are constructing provides a means to fully instrument all of the neurons and their interactions. The model allows for a wide range of signal analysis experimentation, which we hope will help untangle how this neural processing works.","PeriodicalId":125114,"journal":{"name":"2017 IEEE International Conference on Rebooting Computing (ICRC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130471968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Socrates-D: Multicore Architecture for On-Line Learning Socrates-D:在线学习的多核架构

2017 IEEE International Conference on Rebooting Computing (ICRC) Pub Date : 2017-11-01 DOI: 10.1109/ICRC.2017.8123668

Yangjie Qi, Raqibul Hasan, Rasitha Fernando, T. Taha

引用次数: 3

The Superstrider Architecture: Integrating Logic and Memory Towards Non-Von Neumann Computing Superstrider架构:整合逻辑和记忆迈向非冯诺依曼计算

2017 IEEE International Conference on Rebooting Computing (ICRC) Pub Date : 2017-11-01 DOI: 10.1109/ICRC.2017.8123669

S. Srikanth, T. Conte, E. Debenedictis, Jeanine E. Cook

引用次数: 14

Generating Sparse Representations Using Quantum Annealing: Comparison to Classical Algorithms 使用量子退火生成稀疏表示:与经典算法的比较

2017 IEEE International Conference on Rebooting Computing (ICRC) Pub Date : 2017-11-01 DOI: 10.1109/ICRC.2017.8123653

N. T. Nguyen, Amy E. Larson, Garrett T. Kenyon

{"title":"Generating Sparse Representations Using Quantum Annealing: Comparison to Classical Algorithms","authors":"N. T. Nguyen, Amy E. Larson, Garrett T. Kenyon","doi":"10.1109/ICRC.2017.8123653","DOIUrl":"https://doi.org/10.1109/ICRC.2017.8123653","url":null,"abstract":"We use a quantum annealing D-Wave 2X (1,152-qubit) computer to generate sparse representations of Canny-filtered, center-cropped 30x30 CIFAR-10 images. Each binary neuron (qubit) represents a feature kernel obtained initially by imprinting on a randomly chosen 5x5 image patch and then adapted via an off-line Hebbian learning protocol using the sparse solutions generated by the D-Wave. When using binary neurons, the energy function is non-convex (multiple local-minima) and finding a global minimum is NP-hard. Quantum annealing provides a strategy for finding sparse representations that correspond to good local minima of a non-convex cost function. To overcome the severe coupling restrictions between physical qubits on the D-Wave Chimera graph, we use embedding tools to achieve approximately all-to-all connectivity across a reduced number of logical qubits. We assess the sparse representations generated by the D-Wave using both the total energy as well as classification accuracy on a subset of the CIFAR-10 database. The D-Wave 2X outperforms two classical state-of-the-art binary solvers, GUROBI and Chimera-inspired algorithm Hamze-Freitas-Selby (HFS). Specifically, the D-Wave 2X yields lower energy sparse solutions within seconds while the largest problems take over 10 hours for both GUROBI and HFS. We obtained cross-validation classification of 31.02% for the first 4K images using 47 features on the quantum D- Wave 2X.","PeriodicalId":125114,"journal":{"name":"2017 IEEE International Conference on Rebooting Computing (ICRC)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114762395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Physical Constraints on Quantum Circuits 量子电路的物理约束

2017 IEEE International Conference on Rebooting Computing (ICRC) Pub Date : 2017-11-01 DOI: 10.1109/ICRC.2017.8123663

P. Civalleri, F. Corinto, Á. Csurgay

引用次数: 0

Convolutional Drift Networks for Video Classification 用于视频分类的卷积漂移网络

2017 IEEE International Conference on Rebooting Computing (ICRC) Pub Date : 2017-11-01 DOI: 10.1109/ICRC.2017.8123647

Dillon Graham, Seyed Hamed Fatemi Langroudi, Christopher Kanan, D. Kudithipudi

{"title":"Convolutional Drift Networks for Video Classification","authors":"Dillon Graham, Seyed Hamed Fatemi Langroudi, Christopher Kanan, D. Kudithipudi","doi":"10.1109/ICRC.2017.8123647","DOIUrl":"https://doi.org/10.1109/ICRC.2017.8123647","url":null,"abstract":"Analyzing spatio-temporal data like video is a challenging task that requires processing visual and temporal information effectively. Convolutional Neural Networks have shown promise as baseline fixed feature extractors through transfer learning, a technique that helps minimize the training cost on visual information. Temporal information is often handled using hand-crafted features or Recurrent Neural Networks, but this can be overly specific or prohibitively complex. Building a fully trainable system that can efficiently analyze spatio-temporal data without hand-crafted features or complex training is an open challenge. We present a new neural network architecture to address this challenge, the Convolutional Drift Network (CDN). Our CDN architecture combines the visual feature extraction power of deep Convolutional Neural Networks with the intrinsically efficient temporal processing provided by Reservoir Computing. In this introductory paper on the CDN, we provide a very simple baseline implementation tested on two egocentric (first-person) video activity datasets. We achieve video-level activity classification results on-par with state-of-the art methods. Notably, performance on this complex spatio- temporal task was produced by only training a single feed-forward layer in the CDN.","PeriodicalId":125114,"journal":{"name":"2017 IEEE International Conference on Rebooting Computing (ICRC)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122116796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Routing Congestion Aware Cell Library Development for Monolithic 3D ICs 面向单片3D集成电路的路由拥塞感知单元库开发

2017 IEEE International Conference on Rebooting Computing (ICRC) Pub Date : 2017-11-01 DOI: 10.1109/ICRC.2017.8123686

Chen Yan, E. Salman

引用次数: 4