Titlebook: Deep In-memory Architectures for Machine Learning; Mingu Kang,Sujan Gonugondla,Naresh R. Shanbhag Book 2020 Springer Nature Switzerland AG

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Annie Kerguenne,Mara Meisel,Christoph Meinelare heavily constrained in terms of computational resources (storage, processing, and communications), energy, latency, and form factor. This book describes a unique approach to realizing that objective—. (DIMA).

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Introduction,are heavily constrained in terms of computational resources (storage, processing, and communications), energy, latency, and form factor. This book describes a unique approach to realizing that objective—. (DIMA).

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The Deep In-Memory Architecture (DIMA),nspired model of computation. The various stages of analog computations in DIMA are described along with practical circuit and architectural design guidelines. Finally, circuit-aware system models of DIMA’s energy, delay, and functionality are presented and employed to evaluate DIMA’s decision-making accuracy, energy, and latency trade-offs.

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Future Prospects,e architectures for large problem sizes, architectures using emerging memory technology such as RRAM and MRAM, and statistical error compensation techniques for enhancing its energy-latency gains over the von Neumann architecture.

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view of this emerging topic for both the practicing engineer in industry and the researcher in academia. The book is a journey into the exciting world of AI systems in hardware..978-3-030-35973-7978-3-030-35971-3

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PROMISE: A DIMA-Based Accelerator,ficiency of mixed-signal accelerators for specific ML algorithms. PROMISE exposes instruction set mechanisms that allow software control over energy-vs-accuracy trade-offs, and supports compilation of high-level languages down to the hardware.

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