Research Fellow in Brain-inspired Learning Accelerators

Research currently focuses on information processing systems, robotics, telecommunications, and biomedical engineering, but we are looking to establish new research themes. ... backprop …

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FAQs about Research Fellow in Brain-inspired Learning Accelerators Coupon?

Can we accelerate a lightweight brain-inspired learning solution on existing EDGE systems?

In this paper, we propose an efficient framework to accelerate a lightweight brain-inspired learning solution, hyperdimensional computing (HDC), on existing edge systems. Through algorithm-hardware co-design, we optimize the HDC models to run them on the low-power host CPU and machine learning accelerators like Edge TPU. ...

Who contributed to the peer review of 'brain-inspired computing'?

Nature thanks Steve Furber and Yulia Sandamirskaya for their contribution to the peer review of this work. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Mehonic, A., Kenyon, A.J. Brain-inspired computing needs a master plan. ...

Do we need a benchmarking platform for AI inference accelerators?

Several metrics and benchmarking platforms have been published for AI inference accelerators 38, 39 and lifelong learning algorithms 31, but there is a need to identify and develop new benchmarks and workloads suitable for continual learning accelerators on a larger scale, including hardware and algorithmic metrics. ...

How to design a lifelong learning accelerator?

To design a more general-purpose lifelong learning accelerator, the system should have the ability to autonomously self-tune at a finer granularity based on the application, and have the capability to evaluate the trade-offs across the learning features. ...

Who are the members of the neuromorphic AI Lab?

We thank members of the Neuromorphic AI lab, P. Helfer, T. Pandit, V. Karia and S. Hamed Fatemi Langroudi for discussions on this topic. Part of this material is based on research sponsored by the Air Force Research Laboratory under agreement no. FA8750-20-2-1003 through BAA FA8750-19-S-7010. ...

What are the capabilities of AI accelerators?

By studying these methods, we have assembled a list of six desirable capabilities for AI accelerators: on-device learning, resource reassignment within budget, model recoverability, synaptic consolidation, structural plasticity and replay memory. ...