November 17, 2023
November 17, 2023
6th Workshop on Accelerated Machine Learning (AccML)
Co-located with the HiPEAC 2024 Conference
January 17, 2024
CALL FOR CONTRIBUTIONS
The remarkable performance achieved in a variety of application areas (natural language processing, computer vision, games, etc.) has led to the emergence of heterogeneous architectures to accelerate machine learning workloads. In parallel, production deployment, model complexity and diversity pushed for higher productivity systems, more powerful programming abstractions, software and system architectures, dedicated runtime systems and numerical libraries, deployment and analysis tools. Deep learning models are generally memory and computationally intensive, for both training and inference. Accelerating these operations has obvious advantages, first by reducing the energy consumption (e.g. in data centers), and secondly, making these models usable on smaller devices at the edge of the Internet. In addition, while convolutional neural networks have motivated much of this effort, numerous applications and models involve a wider variety of operations, network architectures, and data processing. These applications and models permanently challenge computer architecture, the system stack, and programming abstractions. The high level of interest in these areas calls for a dedicated forum to discuss emerging acceleration techniques and computation paradigms for machine learning algorithms, as well as the applications of machine learning to the construction of such systems.
LINKS TO THE WORKSHOP PAGES
Topics of interest include (but are not limited to):
– Novel ML systems: heterogeneous multi/many-core systems, GPUs and FPGAs;
– Software ML acceleration: languages, primitives, libraries, compilers and frameworks;
– Novel ML hardware accelerators and associated software;
– Emerging semiconductor technologies with applications to ML hardware acceleration;
– ML for the construction and tuning of systems;
– Cloud and edge ML computing: hardware and software to accelerate training and inference;
– Computing systems research addressing the privacy and security of ML-dominated systems;
– ML techniques for more efficient model training and inference (e.g. sparsity, pruning, etc);
– Generative AI and their impact on computational resources
– Giuseppe Desoli (STMicroelectronics): Revolutionizing Edge AI: Enabling Ultra-low-power and High-performance Inference with In-memory Computing Embedded NPUs
Abstract: The increasing demand for Edge AI has led to the development of complex cognitive applications on edge devices, where energy efficiency and compute density are crucial. While HW Neural Processing Units (NPUs) have already shown considerable benefits, the growing need for more complex algorithms demands significant improvements. To address the limitations of traditional Von Neumann architectures, novel designs based on computational memories are being developed by industry and academia. In this talk, we present STMicroelectronics’ future directions in designing NPUs that integrate digital and analog In-Memory Computing (IMC) technology with high-efficiency dataflow inference engines capable of accelerating a wide range of Deep Neural Networks (DNNs). Our approach combines SRAM computational memory and phase change resistive memories, and we discuss the architectural considerations and purpose-designed compiler mapping algorithms required for practical industrial applications and some challenges we foresee in harnessing the potential of In-memory Computing going forward.
– John Kim (KAIST): Domain-Specific Networks for Accelerated Computing
Abstract: Domain-specific architectures are hardware computing engine that is specialized for a particular application domain. As domain-specific architectures become widely used, the interconnection network can become the bottleneck for the system as the system scales. In this talk, I will present the role of domain-specific interconnection networks to enable scalable domain-specific architectures. In particular, I will present the impact of the physical/logical topology of the interconnection network on communication such as AllReduce in domain-specific systems. I will also discuss the opportunity of domain-specific interconnection networks and how they can be leveraged to optimize overall system performance and efficiency. As a case study, I will present the unique design of the Groq software-managed scale-out system and how it adopts architectures from high-performance computing to enable a domain-specific interconnection network.
– Adam Paszke (Google): A Multi-Platform High-Productivity Language for Accelerator Kernels
Abstract: Compute accelerators are the workhorses of modern scientific computing and machine learning workloads. But, their ever increasing performance also comes at a cost of increasing micro-architectural complexity. Worse, it happens at a speed that makes it hard for both compilers and low-level kernel authors to keep up. At the same time, the increased complexity makes it even harder for a wider audience to author high-performance software, leaving them almost entirely reliant on high-level libraries and compilers. In this talk I plan to introduce Pallas: a domain specific language embedded in Python and built on top of JAX. Pallas is highly inspired by the recent development and success of the Triton language and compiler, and aims to present users with a high-productivity programming environment that is a minimal extension over native JAX. For example, kernels can be implemented using the familiar JAX-NumPy language, while a single line of code can be sufficient to interface the kernel with a larger JAX program. Uniquely, Pallas kernels support a subset of JAX program transformations, making it possible to derive a number of interesting operators from a single implementation. Finally, based on our experiments, Pallas can be leveraged for high-performance code generation not only for GPUs, but also for other accelerator architectures such as Google’s TPUs.
– Ayse Coskun (Boston University): ML-Powered Diagnosis of Performance Anomalies in Computer Systems
Abstract: Today’s large-scale computer systems that serve high performance computing and cloud face challenges in delivering predictable performance, while maintaining efficiency, resilience, and security. Much of computer system management has traditionally relied on (manual) expert analysis and policies that rely on heuristics derived based on such analysis. This talk will discuss a new path on designing ML-powered “automated analytics” methods for large-scale computer systems and how to make strides towards a longer term vision where computing systems are able to self-manage and improve. Specifically, the talk will first cover how to systematically diagnose root causes of performance “anomalies”, which cause substantial efficiency losses and higher cost. Second, it will discuss how to identify applications running on computing systems and discuss how such discoveries can help reduce vulnerabilities and avoid unwanted applications. The talk will also highlight how to apply ML in a practical and scalable way to help understand complex systems, demonstrate methods to help standardize study of performance anomalies, discuss explainability of applied ML methods in the context of computer systems, and point out future directions in automating computer system management.
Papers will be reviewed by the workshop’s technical program committee according to criteria regarding the submission’s quality, relevance to the workshop’s topics, and, foremost, its potential to spark discussions about directions, insights, and solutions in the context of accelerating machine learning. Research papers, case studies, and position papers are all welcome.
In particular, we encourage authors to submit work-in-progress papers: To facilitate sharing of thought-provoking ideas and high-potential though preliminary research, authors are welcome to make submissions describing early-stage, in-progress, and/or exploratory work in order to elicit feedback, discover collaboration opportunities, and spark productive discussions.
The workshop does not have formal proceedings.
Submission deadline: November 17, 2023
Notification of decision: December 8, 2023
José Cano (University of Glasgow)
Valentin Radu (University of Sheffield)
José L. Abellán (University of Murcia)
Marco Cornero (DeepMind)
Ulysse Beaugnon (Google)
Juliana Franco (DeepMind)