Single Neuromorphic Memristor closely Emulates Multiple Synaptic Mechanisms for Energy Efficient Neural Networks
CoRR(2024)
摘要
Biological neural networks do not only include long-term memory and weight
multiplication capabilities, as commonly assumed in artificial neural networks,
but also more complex functions such as short-term memory, short-term
plasticity, and meta-plasticity - all collocated within each synapse. Here, we
demonstrate memristive nano-devices based on SrTiO3 that inherently emulate all
these synaptic functions. These memristors operate in a non-filamentary, low
conductance regime, which enables stable and energy efficient operation. They
can act as multi-functional hardware synapses in a class of bio-inspired deep
neural networks (DNN) that make use of both long- and short-term synaptic
dynamics and are capable of meta-learning or "learning-to-learn". The resulting
bio-inspired DNN is then trained to play the video game Atari Pong, a complex
reinforcement learning task in a dynamic environment. Our analysis shows that
the energy consumption of the DNN with multi-functional memristive synapses
decreases by about two orders of magnitude as compared to a pure GPU
implementation. Based on this finding, we infer that memristive devices with a
better emulation of the synaptic functionalities do not only broaden the
applicability of neuromorphic computing, but could also improve the performance
and energy costs of certain artificial intelligence applications.
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