Implementing AI: Hardware Challenges: Memristive Technologies: from Functional Oxides to AI on a Chip - Prof Themis Prodromakis, University of Southampton
The Implementing AI: Hardware Challenges, hosted by KTN and eFutures, is the first event of the Implementing AI webinar series to address the challenges and opportunities that realising AI for hardware present.
There will be presentations from hardware organisations and from solution providers in the morning; followed by Q&A. The afternoon session will consist of virtual breakout rooms, where challenges raised in the morning session can be workshopped.
Artificial Intelligence now impacts every aspect of modern life and is key to the generation of valuable business insights.
Implementing AI webinar series is designed for people involved in the management and implementation of AI based solutions – from developers to CTOs.
Find out more: https://ktn-uk.co.uk/news/just-launched-implementing-ai-webinar-series
Ähnlich wie Implementing AI: Hardware Challenges: Memristive Technologies: from Functional Oxides to AI on a Chip - Prof Themis Prodromakis, University of Southampton
Ähnlich wie Implementing AI: Hardware Challenges: Memristive Technologies: from Functional Oxides to AI on a Chip - Prof Themis Prodromakis, University of Southampton (20)
Implementing AI: Hardware Challenges: Memristive Technologies: from Functional Oxides to AI on a Chip - Prof Themis Prodromakis, University of Southampton
3. Outline
Modern electronics challenges & the AI era needs
Memristors:
• Technology
• Tools & Infrastructure
Application Examples – beyond memory
Conclusion
4. Our AI is as good as our access to data
ENGINEERING CHALLENGE: “The fundamental design of separate memory and
processing places a limit on what can be achieved.”
12. Scientific Reports, 6, 18639, 2016.
Eric Kandel
Nobel Prize
in Physiology 2000
Emulating synapses with memristors
13. 17
Unsupervised learning in probabilistic memristor neural network
Switching vs.
resistive state
relation at fixed
voltage levels ->
Exploit to encode
conditional
probabilities
Desired switching level
Approx. operating V
Unsupervised Learning
Nature Communications, 7, 12611, 2016.
14. 18
• Network shows capability of learning in unsupervised manner and handles mistakes rather well.
• Copes with cases where class centres drift over time.
Unsupervised learning in probabilistic memristor neural network
Unsupervised Learning
Nature Communications, 7, 12611, 2016.
15. 19
Unsupervised learning in probabilistic memristor neural network
Unsupervised Learning
• Whilst ‘learn once’ systems have their uses, ideally one wants something more flexible
(e.g. if class centres drift over time).
Nature Communications, 7, 12611, 2016.
17. 21
Bayesian Inference
“Hardware-Level Bayesian Inference”, Neural Information Processing Systems (NIPS), 2017.
Computing directly in the probability domain
Vector-Matrix-Vector Scalar multiply
31. Unique solutions that address technology gaps across
4 computational pillars
Thinking
AI on a chip
Our chipsets will equip AI systems with sensing, recognition, learning and
reasoning capabilities, paving the way towards “Thinking Machines”.
“AI on chips” will embed intelligence everywhere
34. Bioelectronic Medicines
Feynman: “What I cannot create, I do not understand”
Can we replace parts of our brain?
Can we extend our brain’s capacity?
Can we…???
Augmented Intelligence