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5. Towards Scalable GPU System with Silicon Photonic Chiplet

Author

Li, Chengeng, Jiang, Fan, Chen, Shixi, Li, Xianbin, Liu, Jiaqi, Zhang, Wei, Xu, Jiang, Li, Chengeng, Jiang, Fan, Chen, Shixi, Li, Xianbin, Liu, Jiaqi, Zhang, Wei, and Xu, Jiang

Abstract

GPU-based computing has emerged as a predominant solution for high-performance computing and machine learning applications. The continuously escalating computing demand fore-sees a requirement for larger-scale GPU systems in the future. However, this expansion is constrained by the finite number of transistors per die. Although chip let technology shows potential for building large-scale systems, current chiplet interconnection technologies suffer from limitations in both bandwidth and en-ergy efficiency. In contrast, optical interconnect has ultra-high bandwidth and energy efficiency, and thereby is promising for constructing chiplet-based GPU systems. Yet, previously proposed optical networks lack scalability and cannot be directly applied to existing chiplet-based GPU systems. In this work, we address the challenges of designing large-scale G PU systems with silicon photonic chiplets. We propose GROOT, a group-based optical network that divides the entire system into groups and facilitates resource sharing among the chiplets within each group. Additionally, we design dedicated channel mapping and allocation policies tailored for the request network and the reply network, respectively. Experimental results show that GROOT achieves 48% improvement on performance and 24.5% reduction on system energy consumption over the baseline.

Published
2024

6. SCNoCs: An Adaptive Heterogeneous Multi-NoC with Selective Compression and Power Gating

Author

Jiang, Fan, Li, Chengeng, Chen, Lin, Liu, Jiaqi, Zhang, Wei, Xu, Jiang, Jiang, Fan, Li, Chengeng, Chen, Lin, Liu, Jiaqi, Zhang, Wei, and Xu, Jiang

Abstract

In-network compression has been proposed recently to support efficient communication. However, we find employing compression blindly cannot always pay off since de/compression leads to extra packet transmission delay. We thereby propose selective compression which compresses data adaptively based on network state and predicted compression ratio. Moreover, we observe that simply applying selective compression in a conventional single network is not energy efficient. Therefore, we propose SCNoCs, a heterogeneous Multi-NoC (Main-Net and HelperNet) architecture with the support of selective compression and power gating. SCNoCs can dynamically adjust the policy of selective compression and the utilization degree of the Helper-Net according to the network state at runtime. Experimental results show that our selective compression outperforms conventional compression by 1.5×. Besides, our proposed SCNoCs achieves comparable performance while reducing energy consumption by 43.4%, compared with the baseline.

Published
2024

7. Collaborative Coalescing of Redundant Memory Access for GPU System

Author

Jiang, Fan, Li, Chengeng, Zhang, Wei, Xu, Jiang, Jiang, Fan, Li, Chengeng, Zhang, Wei, and Xu, Jiang

Abstract

GPU-based computing serves as the primary solution driving the performance of HPC systems. However, modern GPU systems encounter performance bottlenecks resulting from heavy memory access traffic and insufficient NoC bandwidth. In this work, we propose a collaborative coalescing mechanism aimed at eliminating redundant memory access and boosting GPU system performance. To achieve this, we design a coalescing unit for each memory partition, effectively merging requests from both inter-cluster and intra-cluster SMs. Additionally, we introduce a hierarchical multicast module to replicate and distribute the coalesced reply messages to multiple destination SMs. Experimental results show that our method achieves 20.6% improvement on performance and 27.1% reduction on NoC traffic over the baseline.

Published
2024

24. Smart Knowledge Transfer-based Runtime Power Management

Author

Chen, Lin, Li, Xiao, Jiang, Fan, Li, Chengeng, Xu, Jiang, Chen, Lin, Li, Xiao, Jiang, Fan, Li, Chengeng, and Xu, Jiang

Abstract

As Moore's law slows down, computing systems must pivot towards higher energy efficiency to continue scaling performance. Reinforcement learning (RL) performs more adaptively than conventional methods in runtime power management under varied hardware configurations and varying software workloads. However, prior works on either model-free or model-based RL approaches face a non-negligible challenge: relearning the policies to adapt to the new environment is unacceptably time-consuming, especially when encountering significant variances in workloads or hardware configurations. Moreover, existing research on accelerating learning has focused on the speedup while largely ignoring the efficiency degradation of the results. In this paper, we present a smart transfer-enabled Q-learning (STQL) approach to boost the learning process and guarantee the learning efficiency through a contradiction checking mechanism, which wisely evicts inappropriate transferred knowledge. Experiments on realistic applications show that the proposed method can speed up the learning process to up to 2.3x and achieve a 6.2% energy-delay product (EDP) reduction compared to the state-of-the-art design. © 2023 EDAA.

Published
2023

25. RONet: Scaling GPU System with Silicon Photonic Chiplet

Author

Li, Chengeng, Jiang, Fan, Chen, Shixi, Li, Xianbin, Liu, Yinyi, Chen, Lin, Li, Xiao, Xu, Jiang, Li, Chengeng, Jiang, Fan, Chen, Shixi, Li, Xianbin, Liu, Yinyi, Chen, Lin, Li, Xiao, and Xu, Jiang

Abstract

Modern GPU systems integrate hundreds of SMs on a single die, and future scaling envisions even more SMs being incorporated. However, the limited number of transistors per die constrains this growth. While current chiplet technology shows promise, its performance is limited by the bandwidth and energy efficiency of existing chiplet interconnect technologies. In contrast, optical interconnects offer ultra-high bandwidth and energy efficiency, making them ideal for high-performance chiplet-based GPUs. This work proposes a novel region-based optical network, called RONet, that divides a chiplet-based GPU system with a 2D Mesh layout into multiple row and column regions, where each region is connected by a separate optical link. Additionally, RONet employs a tuning-free transmission mechanism to further enhance inter-chiplet bandwidth. Experimental results show that RONet achieves 43% improvement on performance and 25.4% reduction on system energy consumption over the baseline. © 2023 IEEE.

Published
2023

34. SONR based layout decomposition and applications

Author

Xu, Rui, primary, Li, Xuan, additional, Jin, Yao, additional, Jiang, Xiaolong, additional, Yang, Lingxue, additional, Ren, Kun, additional, Wu, Yongyu, additional, Gao, Dawei, additional, Du, Chunshan, additional, Wan, Qijian, additional, Hu, Xinyi, additional, Zou, Sihang, additional, Jiang, Fan, additional, and Hong, Le, additional

Published
2022
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41. A-U3D: A Unified 2D/3D CNN Accelerator on the Versal Platform for Disparity Estimation

Author

Zhang, Tianyu, primary, Li, Dong, additional, Wang, Hong, additional, Li, Yunzhi, additional, Ma, Xiang, additional, Luo, Wei, additional, Wang, Yu, additional, Huang, Yang, additional, Li, Yi, additional, Zhang, Yu, additional, Yang, Xinlin, additional, Jia, Xijie, additional, Lin, Qiang, additional, Tian, Lu, additional, Jiang, Fan, additional, Xie, Dongliang, additional, Luo, Hong, additional, and Shan, Yi, additional

Published
2022
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42. Experimental Validation of Single Base Station 5G mm Wave Positioning: Initial Findings

Author

Ge, Yu, primary, Chen, Hui, additional, Jiang, Fan, additional, Zhu, Meifang, additional, Khosravi, Hedieh, additional, Lindberg, Simon, additional, Herbertsson, Hans, additional, Eriksson, Olof, additional, Brunnegard, Oliver, additional, Olsson, Bengt-Erik, additional, Hammarberg, Peter, additional, Tufvesson, Fredrik, additional, Svensson, Lennart, additional, and Wymeersch, Henk, additional

Published
2022
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47. SADN: Learned Light Field Image Compression with Spatial-Angular Decorrelation

Author

Tong, Kedeng, Jin, Xin, Wang, Chen, and Jiang, Fan

Subjects
FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), FOS: Electrical engineering, electronic engineering, information engineering, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Light field image becomes one of the most promising media types for immersive video applications. In this paper, we propose a novel end-to-end spatial-angular-decorrelated network (SADN) for high-efficiency light field image compression. Different from the existing methods that exploit either spatial or angular consistency in the light field image, SADN decouples the angular and spatial information by dilation convolution and stride convolution in spatial-angular interaction, and performs feature fusion to compress spatial and angular information jointly. To train a stable and robust algorithm, a large-scale dataset consisting of 7549 light field images is proposed and built. The proposed method provides 2.137 times and 2.849 times higher compression efficiency relative to H.266/VVC and H.265/HEVC inter coding, respectively. It also outperforms the end-to-end image compression networks by an average of 79.6% bitrate saving with much higher subjective quality and light field consistency.

Published
2022

48. Power Management for Chiplet-Based Multicore Systems Using Deep Reinforcement Learning

Author

Li, Xiao, Chen, Lin, Chen, Shixi, Jiang, Fan, Li, Chengeng, Xu, Jiang, Li, Xiao, Chen, Lin, Chen, Shixi, Jiang, Fan, Li, Chengeng, and Xu, Jiang

Abstract

The trade-off between performance and energy to achieve high energy efficiency has become a critical design issue for computing systems. For emerging chiplet-based multicore systems, the explosive growth of system complexity exacerbates the design challenge to improve the energy efficiency of both processors and power delivery systems (PDSs). Previous works that co-manage processors and PDSs based on reinforcement learning can adapt to dynamic workload variations. However, they face poor scalability and PDS efficiency degradation issues. To tackle the above problems, we propose a deep Q-network (DQN)-based online control scheme for power delivery and consumption co-management of chiplet-based multicore systems. When evaluated on realistic applications, our proposed approach with a centralized DQN agent can achieve on average 4.6% and 33.2% greater energy-delay-product (EDP) reduction over the state-of-art modular Q-learning (MQL) approach and heuristic-based approach, respectively.

Published
2022

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