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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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https://doi.org/10.1007/978-3-031-36103-6is a multi-functional DIMA that implements four different machine learning algorithms—the support vector machine (SVM), template matching (TM), .-nearest neighbor (.-NN), and matched filter (MF), thereby demonstrating DIMA’s versatility. The second IC implements the random forest (RF) algorithm whic
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Hasso Plattner,Christoph Meinel,Larry Leifernts a DIMA-based accelerator called PROMISE, which realizes a high level of programmability for diverse ML algorithms without noticeably losing the efficiency of mixed-signal accelerators for specific ML algorithms. PROMISE exposes instruction set mechanisms that allow software control over energy-v
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https://doi.org/10.1007/978-3-319-40382-3ons. DIMA addresses the high energy and latency costs of data movement between the processor and memory by embedding mixed-signal computations deeply into the periphery of the memory core. This chapter suggests potential future research directions for in-memory computing including the design scalabl
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Characterization of Latin American Universities in the TOP10 of the Rankings in their positioning from 2009 to 2019. However, after the evaluation of the innovation and social criteria, the ten universities coincide in declines in their global positioning from this sub-ranking.
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