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LCTES 2017
Wed 21 - Thu 22 June 2017 Barcelona, Spain
co-located with PLDI 2017

Convolutional neural networks (CNNs) are widely adopted in artificial intelligent systems. In contrast to conventional computingcentric applications, the computational and memory resources of CNN applications are mixed together in the network weights. This incurs a significant amount of data movement, especially for highdimensional convolutions. Although recent embedded 3D-stacked Processing-in-Memory (PIM) architecture alleviates this memory bottleneck to provide fast near-data processing, memory is still a limiting factor of the entire system. An unsolved key challenge is how to efficiently allocate convolutions to 3D-stacked PIMto combine the advantages of both neural and computational processing.

This paper presents Memolution, a compiler-based memoryefficient data allocation strategy for convolutional neural networks on PIM architecture. Memolution offers thread-level parallelism that can fully exploit the computational power of PIMarchitecture. The objective is to capture the characteristics of neural network applications and present a hardware-independent design to transparently allocate CNN applications onto the underlining hardware resources provided by PIM. We demonstrate the viability of the proposed technique using a variety of realistic convolutional neural network applications. Our extensive evaluations show that, Memolution significantly improves performance and the cache utilization compared to the baseline scheme.

Thu 22 Jun

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

10:30 - 12:10
Session 3: Non-Volatile Memory/Processor and RTOSLCTES 2017 at Vertex WS208
Chair(s): Hanjun Kim POSTECH
10:30
25m
Talk
Towards Memory-Efficient Processing-in-Memory Architecture for Convolutional Neural Networks
LCTES 2017
Yi Wang Shenzhen University, Mingxu Zhang Shenzhen University, Jing Yang Harbin Institute of Technology
10:55
25m
Talk
Unified nvTCAM and sTCAM Architecture for Improving Packet Matching Performance
LCTES 2017
Xianzhong Ding Shandong University, Zhiyong Zhang Shandong University, Zhiping Jia Shandong University, Lei Ju Shandong University, Mengying Zhao Shandong University, Huawei Huang The University of Aizu
11:20
25m
Talk
A Lightweight Progress Maximization Scheduler for Non-Volatile Processor Under Unstable Energy Harvesting
LCTES 2017
Chen Pan , Mimi Xie Oklahoma State University, Yongpan Liu Tsinghua University, Yanzhi Wang Syracuse University, Jason Xue City University of Hong Kong, China, Yiran Chen University of Pittsburgh, Jingtong Hu Oklahoma State University
11:45
25m
Talk
OSEK-V: Application-Specific RTOS Instantiation in Hardware
LCTES 2017
Christian Dietrich , Daniel Lohmann Friedrich-Alexander-Universität, Germany