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Inferencing, Fast and Slow with Ultra-scaled Phase-Change Devices

CORDIS · observation · Publication date unknown

Inferencing, Fast and Slow with Ultra-scaled Phase-Change Devices A major challenge for deep learning inference is the high energy demand required to retrieve large amounts of synaptic weight data from memory. One promising approach to address this is the use of conductance-based devices, such as non-volatile phase-change memory, to develop chips with stationary synaptic weights. However, two key obstacles remain: enhancing the computational capabilities and increasing the energy efficiency of these devices. INFUSED tackles both issues through groundbreaking device innovation. By utilizing the physics of ultra-scaled materials, it pushes energy efficiency closer to its theoretical limits. Moreover, it introduces dual neurally-plausible temporal dynamics, combining fast adaptive responses with slow, gradual conductance changes. This reimagines traditional neural network elements like the perceptron for more energy-efficient AI inference. INFUSED specifically aims to: ☞ Dev

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recordType

award

status

SIGNED

region

EU

value

1499800

unit

EUR