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DPACS: Hardware Accelerated Dynamic Neural Network Pruning through Algorithm-Architecture Co-design 2023

DPACS: Hardware Accelerated Dynamic Neural Network Pruning t...

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28th acm International conference on Architectural Support for programming languages and Operating Systems (ASPLOS)

作者： Gao, Yizhao Zhang, Baoheng Qi, Xiaojuan So, Hayden Kwok-Hay Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781450399166

By eliminating compute operations intelligently based on the run time input, dynamic pruning (DP) promises to improve deep neural network inference speed substantially without incurring a major impact on their accuracy. Although many DP algorithms with good pruning performance have been proposed, it remains a challenge to translate these theoretical reductions in compute operations into satisfactory end-to-end speedups in practical real-world implementations. The overhead of identifying operations to be pruned during run time, the need to efficiently process the resulting dynamic dataflow, and the non-trivial memory I/O bottleneck that emerges as the number of compute operations reduces, have all contributed to the challenge of implementing practical DP systems. In this paper, the design and implementation of DPACS are presented to address these challenges. DPACS utilizes a hardware-aware dynamic spatial and channel pruning algorithm in conjunction with a dynamic dataflow engine in hardware to facilitate efficient processing of the pruned network. A channel mask precomputation scheme is designed to reduce memory I/O, and a dedicated inter-layer pipeline is used to achieve efficient indexing and dataflow of sparse activation. Extensive design space exploration has been performed using two architectural variations implemented on FPGA to accelerate multiple networks from the ResNet family on the ImageNet and CIFAR10 dataset across a wide range of pruning ratios. Across the spectrum of configurations, DPACS is able to achieve 1.1x to 3.9x end-to-end speedup over a baseline hardware implementation without pruning. Analysis of the tradeoff among accuracy, compute, and memory I/O performance highlights the importance of algorithm-architecture codesign in developing DP systems.

关键词： dynamic pruning DPACS FPGA hardware acceleration algorithm-architecture co-design

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Explainable-DSE: An Agile and Explainable Exploration of Efficient HW/SW Codesigns of Deep Learning Accelerators Using Bottleneck Analysis 28

Explainable-DSE: An Agile and Explainable Exploration of Eff...

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28th acm International conference on Architectural Support for programming languages and Operating Systems (ASPLOS)

作者： Dave, Shail Nowatzki, Tony Shrivastava, Aviral Arizona State Univ Tempe AZ 85287 USA Univ Calif Los Angeles Los Angeles CA USA

ISBN: (纸本)9798400703942

Effective design space exploration (DSE) is paramount for hardware/software codesigns of deep learning accelerators that must meet strict execution constraints. For their vast search space, existing DSE techniques can require excessive trials to obtain a valid and efficient solution because they rely on black-box explorations that do not reason about design inefficiencies. In this paper, we propose Explainable-DSE - a framework for the DSE of accelerator codesigns using bottleneck analysis. By leveraging information about execution costs from bottleneck models, our DSE is able to identify bottlenecks and reason about design inefficiencies, thereby making bottleneck-mitigating acquisitions in further explorations. We describe the construction of bottleneck models for DNN accelerators. We also propose an API for expressing domain-specific bottleneck models and interfacing them with the DSE framework. Acquisitions of our DSE systematically cater to multiple bottlenecks that arise in executions of multi-functional workloads or multiple workloads with diverse execution characteristics. Evaluations for recent computer vision and language models show that Explainable-DSE mostly explores effectual candidates, achieving codesigns of 6x lower latency in 47x fewer iterations vs. non-explainable DSEs using evolutionary or ML-based optimizations. By taking minutes or tens of iterations, it enables opportunities for runtime DSEs.

关键词： design space exploration domain-specific architectures gray-box optimization bottleneck model hardware/software codesign explainability machine learning and systems

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design and implementation of Office Automation Management System Based on Spring Boot 24

Design and Implementation of Office Automation Management Sy...

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2024 Guangdong-Hong Kong-Macao Greater Bay Area International conference on Education Digitalization and Computer Science, EDCS 2024

作者： Zhao, Penyu Zhang, Di Lei, Sushan School of Information Engineering Xi'an Eurasia University Shaanxi Xi'an710065 China

ISBN: (纸本)9798400710360

This system is developed using Java language, using Spring Boot development framework as the basis of the back-end development framework, using ORM framework MyBatis for database access operations. Develop a system with personnel information management, job management, department management, customized approval process, activity approval, role assignment, permission assignment functions. © 2024 Copyright held by the owner/author(s).

关键词： Java programming language

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On programming Variability with Large language Model-based Assistant 23

On Programming Variability with Large Language Model-based A...

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27th acm International Systems and Software Product Line conference, SPLC 2023

作者： Acher, Mathieu Duarte, José Galindo Jézéquel, Jean-Marc University of Rennes Insa Irisa Inria Iuf Rennes France University of Sevilla Sevilla Spain University of Rennes Irisa Inria Iuf Rennes France

ISBN: (纸本)9798400700910

programming variability is central to the design and implementation of software systems that can adapt to a variety of contexts and requirements, providing increased flexibility and customization. Managing the complexity that arises from having multiple features, variations, and possible configurations is known to be highly challenging for software developers. In this paper, we explore how large language model (LLM)-based assistants can support the programming of *** report on new approaches made possible with LLM-based assistants, like: features and variations can be implemented as prompts;augmentation of variability out of LLM-based domain knowledge;seamless implementation of variability in different kinds of artefacts, programming languages, and frameworks, at different binding times (compile-time or run-time). We are sharing our data (prompts, sessions, generated code, etc.) to support the assessment of the effectiveness and robustness of LLMs for variability-related tasks. © 2023 acm.

关键词： Domain Knowledge

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Ou: Automating the Parallelization of Zero-Knowledge Protocols 23

Ou: Automating the Parallelization of Zero-Knowledge Protoco...

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30th acm SIGSAC conference on Computer and Communications Security (acm CCS)

作者： Sang, Yuyang Luo, Ning Judson, Samuel Chaimberg, Ben Antonopoulos, Timos Wang, Xiao Piskac, Ruzica Shao, Zhong Yale Univ New Haven CT 06520 USA Northwestern Univ Evanston IL USA

ISBN: (纸本)9798400700507

A zero-knowledge proof (ZKP) is a powerful cryptographic primitive used in many decentralized or privacy-focused applications. However, the high overhead of ZKPs can restrict their practical applicability. We design a programming language, Ou, aimed at easing the programmer's burden when writing efficient ZKPs, and a compiler framework, Lian, that automates the analysis and distribution of statements to a computing cluster. Lian uses programming language semantics, formal methods, and combinatorial optimization to automatically partition an Ou program into efficiently sized chunks for parallel ZK-proving and/or verification. We contribute: (1) A front-end language where users can write proof statements as imperative programs in a familiar syntax;(2) A compiler architecture and implementation that automatically analyzes the program and compiles it into an optimized IR that can be lifted to a variety of ZKP constructions;and (3) A cutting algorithm, based on Pseudo-Boolean optimization and Integer Linear programming, that reorders instructions and then partitions the program into efficiently sized chunks for parallel evaluation and efficient state reconciliation.

关键词： programming language Zero-knowledge proofs Parallelization

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Compiling Stan to Generative Probabilistic languages and Extension to Deep Probabilistic programming 2021

Compiling Stan to Generative Probabilistic Languages and Ext...

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42nd acm sigplan International conference on programming language design and implementation (PLDI)

作者： Baudart, Guillaume Burroni, Javier Hirzel, Martin Mandel, Louis Shinnar, Avraham PSL Univ Ecole Normale Super INRIA Paris Paris France UMass Amherst Amherst MA USA IBM Res MIT IBM Watson AI Lab Armonk NY USA

ISBN: (纸本)9781450383912

Stan is a probabilistic programming language that is popular in the statistics community, with a high-level syntax for expressing probabilistic models. Stan differs by nature from generative probabilistic programming languages like Church, Anglican, or Pyro. This paper presents a comprehensive compilation scheme to compile any Stan model to a generative language and proves its correctness. We use our compilation scheme to build two new backends for the Stanc3 compiler targeting Pyro and NumPyro. Experimental results show that the NumPyro backend yields a 2.3x speedup compared to Stan in geometric mean over 26 benchmarks. Building on Pyro we extend Stan with support for explicit variational inference guides and deep probabilistic models. That way, users familiar with Stan get access to new features without having to learn a fundamentally new language.

关键词： Probabilistic programming Semantics Stan Pyro

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mimium: A Self-Extensible programming language for Sound and Music 9

<i>mimium</i>: A Self-Extensible Programming Language for So...

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9th acm sigplan International Workshop on Functional Art, Music, Modelling, and design (FARM)

作者： Matsuura, Tomoya Jo, Kazuhiro Kyushu Univ Grad Sch Design Fukuoka Fukuoka Japan Kyushu Univ Fac Design Fukuoka Fukuoka Japan

ISBN: (纸本)9781450386135

We propose a programming language for music named mimium, which combines temporal-discrete control and signal processing in a single language. mimium has an intuitive imperative syntax and can use stateful functions as Unit Generator in the same way as ordinary function definitions and applications. Furthermore, the runtime performance is made equivalent to that of lower-level languages by compiling the code through the LLVM compiler infrastructure. By using the strategy of adding a minimum number of features for sound to the design and implementation of a general-purpose functional language, mimium is expected to lower the learning cost for users, simplify the implementation of compilers, and increase the self-extensibility of the language. In this paper, we present the basic language specification, semantics for simple task scheduling, the semantics for stateful functions, and the compilation process. mimium has certain specifications that have not been achieved in existing languages. Future works suggested include extending the compiler functionality to combine task scheduling with the functional paradigm and introducing multi-stage computation for parametric replication of stateful functions.

关键词： programming language computer music functional programming signal processing

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JAX Based Parallel Inference for Reactive Probabilistic programming 2022

JAX Based Parallel Inference for Reactive Probabilistic Prog...

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23rd International conference on languages, Compilers, and Tools for Embedded System (LCTES)

作者： Baudart, Guillaume Mandel, Louis Tekin, Reyyan PSL Univ DI ENS Ecole Normale Super CNRSInria Paris France IBM Res Armonk NY USA

ISBN: (纸本)9781450392662

ProbZelus is a synchronous probabilistic language for the design of reactive probabilistic models in interaction with an environment. Reactive inference methods continuously learn distributions over the unobserved parameters of the model from statistical observations. Unfortunately, this inference problem is in general intractable. Monte Carlo inference techniques thus rely on many independent executions to compute accurate approximations. These methods are expensive but can be parallelized. We propose to use JAX to parallelize ProbZelus reactive inference engine. JAX is a recent library to compile Python code which can then be executed on massively parallel architectures such as GPUs or TPUs. In this paper, we describe a new reactive inference engine implemented in JAX and the new associated JAX backend for ProbZelus. We show on existing benchmarks that our new parallel implementation outperforms the original sequential implementation for a high number of particles.

关键词： Probabilistic programming Reactive programming Streaming Inference Parallel Computing Compilation

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Enhancing closures in scala 3 with spores3 13

Enhancing closures in scala 3 with spores3

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13th acm sigplan Scala Symposium, Scala 2022

作者： Haller, Philipp KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781450394635

The use of closures, a core language feature of functional programming languages, has become popular in the context of concurrent and distributed programming. Using closures in a concurrent or distributed setting increases safety hazards, however, due to captured variables. Previous work proposed spores, enhanced closures that increase safety by constraining their environment using types. This paper presents Spores3, a completely new library-based implementation of spores for Scala 3. It is shown how the new design is enabled by a unique combination of several new features in Scala 3. Moreover, Spores3 contributes a new, portable approach to serializing closures based on type classes. Its implementation supports the same serialization approach on both the JVM and the JavaScript backends of Scala. © 2022 Owner/Author.

关键词： Functional programming

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ProSE: The Architecture and design of a Protein Discovery Engine 27

ProSE: The Architecture and Design of a Protein Discovery En...

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27th acm International conference on Architectural Support for programming languages and Operating Systems (ASPLOS)

作者： Robson, Eyes Xu, Ceyu Wills, Lisa Wu Univ Calif Berkeley Berkeley CA 94720 USA Duke Univ Durham NC USA

ISBN: (纸本)9781450392051

Protein language models have enabled breakthrough approaches to protein structure prediction, function annotation, and drug discovery. A primary limitation to the widespread adoption of these powerful models is the high computational cost associated with the training and inference of these models, especially at longer sequence lengths. We present the architecture, microarchitecture, and hardware implementation of a protein design and discovery accelerator, ProSE (Protein Systolic Engine). ProSE has a collection of custom heterogeneous systolic arrays and special functions that process transfer learning model inferences efficiently. The architecture marries SIMD-style computations with systolic array architectures, optimizing coarse-grained operation sequences across model layers to achieve efficiency without sacrificing generality. ProSE performs Protein BERT inference at up to 6.9x speedup and 48x power efficiency (performance/Watt) compared to one NVIDIA A100 GPU. ProSE achieves up to 5.5 x (12.7x) speedup and 173x (249x) power efficiency compared to TPUv3 (TPUv2).

关键词： Accelerators Neural Networks Transformers Domain-Specific Architecture NLP Protein design

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