Hybrid electronic-photonic architectures for brain-inspired computing
HYBRAIN aims to develop a brain-inspired hybrid architecture combining integrated photonics and unconventional electronics for ultrafast, energy-efficient edge AI inference.
Projectdetails
Introduction
As artificial intelligence (AI) proliferates, hardware systems that can perform inference at ultralow latency, high precision, and low power are crucial and urgently required to deal especially quasi-locally, i.e., in the edge with massive and heterogeneous data. These systems must respond in real time, avoid unintended consequences, and function in complex and often unpredictable environments.
Limitations of Conventional Systems
Conventional digital electronics and the associated computer architecture are unable to meet these stringent requirements with sub-ms latency inference and a sub-10W power budget. This is particularly evident when using convolutional neural networks (CNNs) on benchmarks such as ImageNet classification.
HYBRAIN's Vision
HYBRAIN's vision is to realize a pathway for a radical new technology with ultrafast (~1 microsecond) and energy-efficient (~1 watt) edge AI inference. This will be based on a world-first, brain-inspired hybrid architecture of integrated photonics and unconventional electronics.
Brain-Inspired Architecture
The deeply entwined memory and processing, similar to that in the mammalian brain, obviates the need to shuttle around synaptic weights. The most stringent latency bottleneck in CNNs occurs in the initial convolution layers.
Innovative Approach
Our approach will take advantage of the ultrahigh throughput and low latency of photonic convolutional processors (PCPs) employing novel phase-change materials in these initial layers to radically speed up processing.
- Their output is processed using:
- Cascaded electronic linear classifier layers
- Nonlinear classifier layers based on memristive (phase-change memory) crossbar arrays
- Dopant network processing units
Collaborative Efforts
HYBRAIN's science-towards-technology breakthrough brings together the world's top research groups from academia and industry in complementary technology platforms. Each of these platforms is already highly promising, but by integrating them, HYBRAIN will have a transformative effect on overcoming existing barriers of latency and energy consumption.
Future Applications
This integration will enable a whole new spectrum of edge AI applications throughout society.
Financiële details & Tijdlijn
Financiële details
Subsidiebedrag | € 1.672.528 |
Totale projectbegroting | € 1.672.528 |
Tijdlijn
Startdatum | 1-5-2022 |
Einddatum | 30-4-2026 |
Subsidiejaar | 2022 |
Partners & Locaties
Projectpartners
- UNIVERSITEIT TWENTEpenvoerder
- UNIVERSITAET MUENSTER
- TRUST-IT SERVICES SRL
- RUPRECHT-KARLS-UNIVERSITAET HEIDELBERG
- IBM RESEARCH GMBH
- THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD
Land(en)
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The MEMBRAIN project aims to develop self-organizing memristive nanonetworks for efficient, nature-inspired computing that mimics biological neural circuits, enhancing adaptability and intelligence.
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Three dimensional INtegrated PhotonIcS to RevolutionizE deep Learning
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Heterogeneous integration of imprecise memory devices to enable learning from a very small volume of noisy data
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ANalogue In-Memory computing with Advanced device TEchnology
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