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  1. Data redundancy is ubiquitous in the inputs and intermediate results of Deep Neural Networks (DNN) . It offers many significant opportunities for improving DNN performance and efficiency and has been explored in a large body of work. These studies have scattered in many venues across several years. The targets they focus on range from images to videos and texts, and the techniques they use to detect and exploit data redundancy also vary in many aspects. There is not yet a systematic examination and summary of the many efforts, making it difficult for researchers to get a comprehensive view of the prior work, the state of the art, differences and shared principles, and the areas and directions yet to explore. This article tries to fill the void. It surveys hundreds of recent papers on the topic, introduces a novel taxonomy to put the various techniques into a single categorization framework, offers a comprehensive description of the main methods used for exploiting data redundancy in improving multiple kinds of DNNs on data, and points out a set of research opportunities for future exploration. 
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    Free, publicly-accessible full text available October 31, 2024
  2. As more apps embrace AI, it is becoming increasingly common that multiple Deep Neural Networks (DNN)-powered apps may run at the same time on a mobile device. This paper explores scheduling in such multi-instance DNN scenarios, on general open mobile systems (e.g., common smartphones and tablets). Unlike closed systems (e.g., autonomous driving systems) where the set of co-run apps is known beforehand, the user of an open mobile system may install or uninstall arbitrary apps at any time, and a centralized solution is subject to adoption barriers. This work proposes the first-known decentralized application-level scheduling mechanism to address the problem. By leveraging the adaptivity of Deep Reinforcement Learning, the solution is shown to make the scheduling of co-run apps converge to a Nash equilibrium point, yielding a good balance of gains among the apps. The solution moreover automatically adapts to the running environment and the underlying OS and hardware. Experiments show that the solution consistently produces significant speedups and energy savings across DNN workloads, hardware configurations, and running scenarios. 
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  3. More specialized chips are exploiting available high transistor density to expose parallelism at a large scale with more intricate instruction sets. This paper reports on a compilation system GCD^2 , developed to support complex Deep Neural Network (DNN) workloads on mobile DSP chips. We observe several challenges in fully exploiting this architecture, related to SIMD width, more complex SIMD/vector instructions, and VLIW pipeline with the notion of soft dependencies. GCD^2 comprises the following contributions: 1) development of matrix layout formats that support the use of different novel SIMD instructions, 2) formulation and solution of a global optimization problem related to choosing the best instruction (and associated layout) for implementation of each operator in a complete DNN, and 3) SDA, an algorithm for packing instructions with consideration for soft dependencies. These solutions are incorporated in a complete compilation system that is extensively evaluated against other systems using 10 large DNN models. Evaluation results show that GCD^2 outperforms two product-level state-of-the-art end-to-end DNN execution frameworks (TFLite and Qualcomm SNPE) that support mobile DSPs by up to 6.0× speedup, and outperforms three established compilers (Halide, TVM, and RAKE) by up to 4.5×,3.4× and 4.0× speedup, respectively. GCD^2 is also unique in supporting, real-time execution of certain DNNs, while its implementation enables two major DNNs to execute on a mobile DSP for the first time. 
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  4. This article points out an important threat that application-level Garbage Collection (GC) creates to the use of non-volatile memory (NVM). Data movements incurred by GC may invalidate the pointers to objects on NVM and, hence, harm the reusability of persistent data across executions. The article proposes the concept of movement-oblivious addressing (MOA), and develops and compares three novel solutions to materialize the concept for solving the addressability problem. It evaluates the designs on five benchmarks and a real-world application. The results demonstrate the promise of the proposed solutions, especially hardware-supported Multi-Level GPointer, in addressing the problem in a space- and time-efficient manner. 
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