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Investigation of synapse formation by novel nanoscale imaging techniques
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A novel synaptic plasticity rule explains homeostasis of neuromuscular transmission | eLife
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To test our architecture, we trained both spiking and a non-spiking neural networks. Our data selected is a popular one — the MNIST data set of handwritten digits and our task is handwritten digit recognition — essentially our network needs to recognize what number is appearing from the handwritten images. In both cases, we see that the multi-memristive synapse significantly outperforms the conventional differential architectures with 2 devices, clearly illustrating the effectiveness of the proposed architecture. A highlight of the work is an experimental demonstration of the multi-memristive synaptic architecture in a spiking neural network using more than 1 million phase-change memory devices.
The architecture is applicable to a wide range of neural networks and memristive technologies and is crossbar-compatible. The proposed architecture and its experimental demonstration are a significant step towards the realization of highly efficient, large-scale neural networks based on memristive devices with typical, experimentally observed non-ideal characteristics.
I am extremely proud to announce that our work is now published in Nature Communications! Nature Communications neuromorphic paper PCM. Previous Post. Next Post. AI Hardware. Continue reading. IBM researchers introduce accumulation bit-width scaling, addressing a critical need in ultra-low-precision hardware for training deep neural networks. IBM Research shares new results at SysML that push the envelope for deep learning inference, enabling high accuracy down to 2-bit precision.
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