Approximate communication is being seriously considered as an effective technique for reducing power consumption and improving the communication efficiency of network-on-chips (NoCs). A major problem faced by these techniques is quality control: how do we ensure that the network will transmit data with sufficient accuracy for applications to produce acceptable results? Previous methods that addressed this issue require each application to calculate the approximation level for every piece of approximable data, which takes hundreds of cycles. So the approximation information is often not available when a request packet is transmitted. Therefore, the reply packet with the approximable data is transmitted with unnecessarily absolute accuracy, reducing the effectiveness of approximate communication.
In this paper, we propose a hardware-based quality management framework for approximate communication to minimize the time needed for the approximation level calculation. The proposed framework employs a configuration algorithm to continuously adjust the quality of every piece of data based on the difference between the output quality and the application's quality requirement. When the proposed framework is implemented in a network, every request packet can be transmitted with the updated approximation level. This framework results in fewer flits in each data packet and reduces traffic in NoCs while meeting the quality requirements of applications. Our cycle-accurate simulation using the AxBench benchmark suite shows that the proposed online quality management framework can reduce network latency by up to 52% and dynamic power consumption by 59% compared to previous approximate communication techniques while ensuring 95% output quality. This hardware-software codesign incurs 1% area overhead over previous techniques.
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Deluge: Achieving Superior Efficiency, Throughput, and Scalability with Actor Based Streaming on Migrating Threads
Applications where streams of data are passed through large data structures are becoming of increasing importance. For instance network intrusion detection and cyber security as a whole rely on real time analysis of network traffic. Unfortunately, when implemented on conventional architectures such applications become horribly inefficient, especially when attempts are made to scale up performance via some sort of parallelism. An earlier paper discussed streaming anomaly detection within a stream having an unbounded range of keys on the Lucata migrating thread architecture. In this paper we introduce \textit{Deluge}, a new implementation that addresses several inadequacies of previous designs and seeks to more directly target the hardware efficiencies inherent to migratory execution within a PGAS address space. Deluge achieves major improvements in hardware efficiency, throughput, and scalability over previous implementations.
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- Award ID(s):
- 1822939
- NSF-PAR ID:
- 10298917
- Date Published:
- Journal Name:
- IEEE High Performance Extreme Computing Conference
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Autonomous mobile robots (AMRs) have been widely utilized in industry to execute various on-board computer-vision applications including autonomous guidance, security patrol, object detection, and face recognition. Most of the applications executed by an AMR involve the analysis of camera images through trained machine learning models. Many research studies on machine learning focus either on performance without considering energy efficiency or on techniques such as pruning and compression to make the model more energy-efficient. However, most previous work do not study the root causes of energy inefficiency for the execution of those applications on AMRs. The computing stack on an AMR accounts for 33% of the total energy consumption and can thus highly impact the battery life of the robot. Because recharging an AMR may disrupt the application execution, it is important to efficiently utilize the available energy for maximized battery life. In this paper, we first analyze the breakdown of power dissipation for the execution of computer-vision applications on AMRs and discover three main root causes of energy inefficiency: uncoordinated access to sensor data, performance-oriented model inference execution, and uncoordinated execution of concurrent jobs. In order to fix these three inefficiencies, we propose E2M, an energy-efficient middleware software stack for autonomous mobile robots. First, E2M regulates the access of different processes to sensor data, e.g., camera frames, so that the amount of data actually captured by concurrently executing jobs can be minimized. Second, based on a predefined per-process performance metric (e.g., safety, accuracy) and desired target, E2M manipulates the process execution period to find the best energy-performance trade off. Third, E2M coordinates the execution of the concurrent processes to maximize the total contiguous sleep time of the computing hardware for maximized energy savings. We have implemented a prototype of E2M on a real-world AMR. Our experimental results show that, compared to several baselines, E2M leads to 24% energy savings for the computing platform, which translates into an extra 11.5% of battery time and 14 extra minutes of robot runtime, with a performance degradation lower than 7.9% for safety and 1.84% for accuracy.more » « less
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Accepted Manuscript1.0
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