{"product_id":"9783032230997","title":"Minimizing Data Movement and Parameter Count Across the Machine Learning Stack Everything is a Matrix","description":"\u003ch3\u003eSynthesis Lectures on Computer Science\u003c\/h3\u003e\u003ch1\u003eMinimizing Data Movement and Parameter Count Across the Machine Learning Stack\u003c\/h1\u003e\u003ch2\u003eEverything is a Matrix\u003c\/h2\u003e\u003ch3\u003eAndrew Sabot\u003c\/h3\u003e\u003cdiv\u003e\u003cb\u003eComputers \/ Artificial Intelligence \/ General\u003c\/b\u003e\u003c\/div\u003e\u003cbr\u003e\u003cdiv\u003e\u003cp class=\"MsoNormal\"\u003eThis book provides a focused, research-forward guide to making large AI models efficient in practice and also presents an array of novel techniques to reduce memory footprint, accelerate computation, and improve overall hardware utilization. The author demonstrates that substantial efficiency gains can be achieved by rethinking how data is computed, stored, and compressed, with a special focus on matrices, the core computational structure underpinning both scientific computing and neural networks. Modern AI models run on huge grids of numbers (matrices\/tensors), and their speed and affordability depend on how those numbers are arranged and processed on real hardware (GPUs\/TPUs\/CPUs). This book explains practical methods to skip unnecessary work (structured sparsity), move data efficiently (gather\/scatter), and shrink models without losing accuracy (block distillation) so that AI systems can use less memory, less time, and less energy without sacrificing quality. In addition, the book shows how to turn algorithmic ideas into hardware-aware speedups on GPUs\/TPUs. Readers will learn when sparsity pays off, how to schedule irregular workloads, and how to recover accuracy in compressed models. Case studies illustrate end-to-end design choices, evaluation, and pitfalls.\u003cspan style=\"mso-spacerun: yes;\"\u003e \u003c\/span\u003eThe result is a coherent perspective that bridges theory, compilers\/run times, and real-world deployment.\u003c\/p\u003e\u003c\/div\u003e\u003cdiv\u003e\u003cp class=\"MsoNormal\"\u003eAndrew Sabot, Ph.D., is a Software Engineer working on Machine Learning at Google. He received his Ph.D. (2025) and M.S. (2021) in Computer Science from Harvard University. Dr. Sabot’s work focuses on the intersection of hardware-aware kernels, \u003cspan style=\"mso-spacerun: yes;\"\u003e \u003c\/span\u003emodel compression, and transformer inference acceleration to enable the sustainable deployment of state-of-the-art AI.\u003c\/p\u003e\u003c\/div\u003e\u003cbr\u003e\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublication Date: \u003c\/td\u003e\n\u003ctd\u003e02 July 2026\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublisher: \u003c\/td\u003e\n\u003ctd\u003eSpringer Nature Switzerland\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eImprint: \u003c\/td\u003e\n\u003ctd\u003eSpringer\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eISBN-13: \u003c\/td\u003e\n\u003ctd\u003e9783032230997\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFormat: \u003c\/td\u003e\n\u003ctd\u003eHardback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePage Count: \u003c\/td\u003e\n\u003ctd\u003e110\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e","brand":"Springer Nature Switzerland","offers":[{"title":"Default Title","offer_id":46548034650252,"sku":"9783032230997","price":40.49,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9783032230997.jpg?v=1780601551","url":"https:\/\/fh90cf-fv.myshopify.com\/products\/9783032230997","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}