publication . Conference object . 2019

Computational memory-based inference and training of deep neural networks

Abu Sebastian; Irem Boybat; Martino Dazzi; Iason Giannopoulos; V. P. Jonnalagadda; Vinay Joshi; Geethan Karunaratne; Benedikt Kersting; Riduan Khaddam-Aljameh; S. R. Nandakumar; ...
  • Published: 26 Jul 2019
  • Publisher: IEEE
Abstract
In-memory computing is an emerging computing paradigm where certain computational tasks are performed in place in a computational memory unit by exploiting the physical attributes of the memory devices. Here, we present an overview of the application of in-memory computing in deep learning, a branch of machine learning that has significantly contributed to the recent explosive growth in artificial intelligence. The methodology for both inference and training of deep neural networks is presented along with experimental results using phase-change memory (PCM) devices.
Subjects
ACM Computing Classification System: Hardware_MEMORYSTRUCTURES
free text keywords: Explosive material, In-Memory Processing, Inference, Very-large-scale integration, Deep neural networks, Deep learning, Computer science, Artificial intelligence, business.industry, business
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