AIM 2017 - September 10th - Portland, Oregon, USA



8:50am - 9:00am Welcome and introduction to AIM workshop
9:00am - 10:00am Keynote 1
Accelerating Persistent Neural Networks at Datacenter Scale Jeremy Fowers Microsoft Research
10:00am - 10:30am Break
10:30am - 11:00am Convolutional Neural networks for Text Classification using Intel Nervana Neon Kripa Sankaranarayanan, Yinyin Liu Intel Corp
11:00am - 11:30am Layer-wise Performance Bottleneck Analysis of Deep Neural Networks Hengyu Zhao, Colin Weinshenker*, Mohamed Ibrahim*, Adwait Jog*, Jishen Zhao University of California Santa Cruz, *The College of William and Mary
11:30am - 12:00pm Optimizing neon Deep Learning Framework for Intel Architectures Wei Wang, Peng Zhang, Jayaram Bobba, Dawn Stone, Menglin Wu, Xiaohui Zhao, Mingfei Ma, Wenting Jiang, Jason Ye, Huma Abidi, Jennifer Myers, Hanlin Tang, Evren Tumer Intel Corp
12:00pm - 13:30pm Lunch break
13:30pm - 14:30pm Keynote 2
AI: A Platform Perspective Mohan J Kumar Data Center Group Intel Corp
14:30pm - 14:45pm Break
14:45pm - 15:15pm Flexible On-chip Memory Architecture for DCNN Accelerators Arash Azizimazreah, Lizhong Chen Oregon State University
15:15pm - 15:45pm Accelerating TensorFlow on Modern Intel Architectures Elmoustapha Ould-Ahmed-Vall, Mahmoud Abuzaina, Md Faijul Amin, Jayaram Bobba, Roman S Dubtsov, Evarist M Fomenko, Mukesh Gangadhar, Niranjan Hasabnis, Jing Huang, Deepthi Karkada, Young Jin Kim, Srihari Makineni, Dmitri Mishura, Karthik Raman, AG Ramesh, Vivek V Rane, Michael Riera, Dmitry Sergeev, Vamsi Sripathi, Bhavani Subramanian, Lakshay Tokas, Antonio C Valles Intel Corp
15:45pm - 16:15pm Understanding Large-Scale I/O Workload Characteristics via Deep Neural Networks Jinyoung Moon, Myoungsoo Jung Yonsei University, Korea


With the explosion of data creation and uploading across internet of things, hand-held devices and PCs, and cloud and enterprise, there is truly a big opportunity to apply machine learning and deep learning techniques on these terabytes of massive data and deliver breakthroughs in many domains. Deep learning in computer vision, speech recognition, video processing, etc., have sped up advances in many applications from the domains of manufacturing, robotics, business intelligence, autonomous driving, precision medicine, and digital surveillance, to name a few. Traditional machine learning algorithms such as Support Vector Machine, Principal Components Analyses, Alternate Least Squares, K-Means, and Decision Trees are ever present in product recommendations for online users, fraud detection, and financial services. There is a race to design parallel architectures to innovate, cover end-to-end workflows with low time to train while hitting state-of-the-art or higher accuracies without overfitting, low latency inferencing etc., all the while having good TCO, perf/watt and compute and memory efficiencies. Architectural innovation in CPUs, GPUs, FPGAs, ASICs, memories, and on-chip interconnects are needed with utmost urgency by these neural network and mathematical algorithms to attain their latency and accuracy requirements. Mixed and/or low precision arithmetic, high bandwidth stacked DRAMs, systolic array processing, vector extensions in many cores and multi-cores, special neural network instructions and sparse and dense data structures are some of the ways in which GEMM operations, Winograd convolutions, RELUs, fully connected layers etc., are optimally run to achieve expected accuracies and training and inference requirements.

This workshop aims to bring computer architecture, compiler, AI and machine learning/deep learning researchers as well as domain experts together, to produce research that target the confluence of these disciplines. It will be a venue for discussion and brainstorming of the topics related to these areas. The topics of interest include, but are not limited to:

Submission Guidelines

All manuscripts will be reviewed by at least three members of the program committee. Submissions should be a complete manuscript (not to exceed 6 pages of single spaced text in the ACM format, including figures and tables). Submissions should be in the PDF format. Templates for paper preparation can be found in ACM. Please follow this link to submit your paper.

Important Dates:

Program Committee:

Web/Publicity Chair:

Submission Chair: