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تسجيل مستخدم جديدMemristor-based Synaptic Sampling Machines
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Synaptic Sampling Machine (SSM) is a type of neural network model that considers biological unreliability of the synapses. We propose the circuit design of the SSM neural network which is realized through the memristive-CMOS crossbar structure with the synaptic sampling cell (SSC) being used as a basic stochastic unit. The increase in the edge computing devices in the Internet of things era, drives the need for hardware acceleration for data processing and computing. The computational considerations of the processing speed and possibility for the real-time realization pushes the synaptic sampling algorithm that demonstrated promising results on software for hardware implementation.
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Brain-inspired neuromorphic computing which consist neurons and synapses, with an ability to perform complex information processing has unfolded a new paradigm of computing to overcome the von Neumann bottleneck. Electronic synaptic memristor devices
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In this reply, we will provide our impersonal, point-to-point responses to the major criticisms (in bold and underlined) in arXiv:1909.12464. Firstly, we will identify a number of (imperceptibly hidden) mistakes in the Comment in understanding/interp
reting our physical model. Secondly, we will use a 3rd-party experiment carried out in 1961 (plus other 3rd-party experiments thereafter) to further support our claim that our invented Phi memristor is memristive in spite of the existence of a parasitic inductor effect. Thirdly, we will analyse this parasitic effect mathematically, introduce our work-in-progress (in nanoscale) and point out that this parasitic inductor effect should not become a big worry since it can be completely removed in the macro-scale devices and safely neglected in the nano-scale devices.
Memristor crossbars are circuits capable of performing analog matrix-vector multiplications, overcoming the fundamental energy efficiency limitations of digital logic. They have been shown to be effective in special-purpose accelerators for a limited
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