DOI: https://doi.org/10.29363/nanoge.neumatdecas.2023.012
Publication date: 9th January 2023
For many practical tasks, that involve real-time processing of sensory data and closed-loop interactions with the environment, conventional artificial intelligence neural network accelerators cannot match the performance of biological ones.
One of the reasons for this gap is that neural computation in biological systems is organized in a way that is very different from the way it is implemented in today's deep networks. In biological neural systems computation is tightly linked to the properties of their computational embodiment, to the physics of their computing elements and to their temporal dynamics.
Recently developed brain-inspired hardware architectures that emulate the biophysics of real neurons and synapses represent a promising technology for implementing alternative computing paradigms that bridge this gap. Memristive devices are a key element of this technology.
In this talk I will present hybrid analog/digital microelectronic and memristive circuits that use their physics directly emulate the biophysics of the neural processes and memory elements they model, and brain-inspired architectures that integrate massively parallel arrays of such circuits to implement on-chip on-line learning and computation.
I will discuss the advantages and disadvantages of these types of computing architectures and argue that they represent a promising approach for applications that need to process input data as it arrives in real-time without having to use eternal memory storage.
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