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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Marcel Taeumel,Robert Hirschfeldrobust deep in-memory SVM classifier prototype in a 65 nm CMOS that uses a standard 16 kB 6T SRAM is presented. This IC employs an on-chip stochastic gradient descent (SGD)-based trainer that adapts not only to chip-specific variations in PVT but also to data statistics in order to further enhance DIMA’s energy efficiency.

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https://doi.org/10.1007/978-3-030-35971-3machine learning in hardware; analog in-memory architectures; Deep In-memory Architecture; Shannon-insp

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Endemic

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Mapping Inference Algorithms to DIMA,ork (CNN) and a sparse distributed memory (SDM) to DIMA is demonstrated. Algorithmic opportunities such as the use of error-aware training in a DIMA-based CNN and the use of ensemble decision-making in SDM can be exploited to compensate for non-ideal analog computations in DIMA leading to even greater energy savings.

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Annie Kerguenne,Mara Meisel,Christoph Meinele industries these days. Such tasks are realized in the Cloud today due to the availability of sufficient computational resources. However, there is growing interest in embedding data analytics into sensor-rich platforms at the Edge including wearables, autonomous vehicles, personal biomedical devic

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