Hardware-Aware Probabilistic Machine Learning Models - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Hardware-Aware Probabilistic Machine Learning Models write by Laura Isabel Galindez Olascoaga. This book was released on 2021-05-19. Hardware-Aware Probabilistic Machine Learning Models available in PDF, EPUB and Kindle. This book proposes probabilistic machine learning models that represent the hardware properties of the device hosting them. These models can be used to evaluate the impact that a specific device configuration may have on resource consumption and performance of the machine learning task, with the overarching goal of balancing the two optimally. The book first motivates extreme-edge computing in the context of the Internet of Things (IoT) paradigm. Then, it briefly reviews the steps involved in the execution of a machine learning task and identifies the implications associated with implementing this type of workload in resource-constrained devices. The core of this book focuses on augmenting and exploiting the properties of Bayesian Networks and Probabilistic Circuits in order to endow them with hardware-awareness. The proposed models can encode the properties of various device sub-systems that are typically not considered by other resource-aware strategies, bringing about resource-saving opportunities that traditional approaches fail to uncover. The performance of the proposed models and strategies is empirically evaluated for several use cases. All of the considered examples show the potential of attaining significant resource-saving opportunities with minimal accuracy losses at application time. Overall, this book constitutes a novel approach to hardware-algorithm co-optimization that further bridges the fields of Machine Learning and Electrical Engineering.
Hardware-aware Algorithms for Efficient Machine Learning
Hardware-aware Algorithms for Efficient Machine Learning - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Hardware-aware Algorithms for Efficient Machine Learning write by Tri Dao Phuc Quang. This book was released on 2023. Hardware-aware Algorithms for Efficient Machine Learning available in PDF, EPUB and Kindle. Machine learning (ML) training will continue to grow to consume more cycles, their inference will proliferate on more kinds of devices, and their capabilities will be used in more domains. Some goals central to this future are to make ML models efficient so they remain practical to train and deploy, and to unlock new application domains with new capabilities. We describe some recent developments in hardware-aware algorithms to improve the efficiency-quality tradeoff of ML models and equip them with long context. In Chapter 2, we focus on structured sparsity, a natural approach to mitigate the extensive compute and memory cost of large ML models. We describe a line of work on learnable fast transforms that, thanks to their expressiveness and efficiency, yields some of the first sparse training methods to speed up large models in wall-clock time (2x) without compromising their quality. In Chapter 3, we focus on efficient Transformer training and inference for long sequences. We describe FlashAttention, a fast and memory-efficient algorithm to compute attention with no approximation. By careful accounting of reads/writes between different levels of memory hierarchy, FlashAttention is 2-4x faster and uses 10-20x less memory compared to the best existing attention implementations, allowing us to train higher-quality Transformers with 8x longer context. FlashAttention is now widely used in some of the largest research labs and companies. In Chapter 4, we examine state-space models, a promising architecture designed for long-range memory. As we seek to understand why early state-space models did not perform well on language modeling tasks, we propose simple multiplicative interaction that expands their expressiveness. We also design hardware-friendly algorithms to train them. As a result, we are able to train state-space models to multi-billion parameter scale, demonstrating a new kind of model competitive with the dominant Transformers in language modeling. We conclude with some exciting directions in ML and systems, such as software-hardware co-design, structured sparsity for scientific AI, and long context for new AI workflows and modalities.
IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning
IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning write by Joao Gama. This book was released on 2021-01-09. IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning available in PDF, EPUB and Kindle. This book constitutes selected papers from the Second International Workshop on IoT Streams for Data-Driven Predictive Maintenance, IoT Streams 2020, and First International Workshop on IoT, Edge, and Mobile for Embedded Machine Learning, ITEM 2020, co-located with ECML/PKDD 2020 and held in September 2020. Due to the COVID-19 pandemic the workshops were held online. The 21 full papers and 3 short papers presented in this volume were thoroughly reviewed and selected from 35 submissions and are organized according to the workshops and their topics: IoT Streams 2020: Stream Learning; Feature Learning; ITEM 2020: Unsupervised Machine Learning; Hardware; Methods; Quantization.
Physics-aware, Probabilistic Machine Learning in the Small Data Regime
Physics-aware, Probabilistic Machine Learning in the Small Data Regime - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Physics-aware, Probabilistic Machine Learning in the Small Data Regime write by Sebastian Johannes Kaltenbach. This book was released on 2023. Physics-aware, Probabilistic Machine Learning in the Small Data Regime available in PDF, EPUB and Kindle.
Efficient Execution of Irregular Dataflow Graphs
Efficient Execution of Irregular Dataflow Graphs - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Efficient Execution of Irregular Dataflow Graphs write by Nimish Shah. This book was released on 2023-08-14. Efficient Execution of Irregular Dataflow Graphs available in PDF, EPUB and Kindle. This book focuses on the acceleration of emerging irregular sparse workloads, posed by novel artificial intelligent (AI) models and sparse linear algebra. Specifically, the book outlines several co-optimized hardware-software solutions for a highly promising class of emerging sparse AI models called Probabilistic Circuit (PC) and a similar sparse matrix workload for triangular linear systems (SpTRSV). The authors describe optimizations for the entire stack, targeting applications, compilation, hardware architecture and silicon implementation, resulting in orders of magnitude higher performance and energy-efficiency compared to the existing state-of-the-art solutions. Thus, this book provides important building blocks for the upcoming generation of edge AI platforms.
