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Creators/Authors contains: "Soures, Nicholas"

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  1. ; ; (, Computer)
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  2. ; ; (, Proceedings of the AAAI Symposium Series)
    Lifelong learning, which refers to an agent's ability to continuously learn and enhance its performance over its lifespan, is a significant challenge in artificial intelligence (AI), that biological systems tackle efficiently. This challenge is further exacerbated when AI is deployed in untethered environments with strict energy and latency constraints. We take inspiration from neural plasticity and investigate how to leverage and build energy-efficient lifelong learning machines. Specifically, we study how a combination of neural plasticity mechanisms, namely neuromodulation, synaptic consolidation, and metaplasticity, enhance the continual learning capabilities of AI models. We further co-design architectures that leverage compute-in-memory topologies and sparse spike-based communication with quantization for the edge. Aspects of this co-design can be transferred to federated lifelong learning scenarios. 
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  3. ; ; ; ; ; ; ; ; ; ; et al (, Proceedings Volume 10984, Quantum Information Science, Sensing, and Computation XI; 109840J)
    Unitary operations using linear optics have many applications within the quantum and neuromorphic space. In silicon photonics, using networks of simple beam splitters and phase shifters have proven sufficient to realize large-scale arbitrary unitaries. While this technique has shown success with high fidelity, the grid physically scales with an upper bound of O(n2). Consequently, we propose to considerably reduce the footprint by using multimode interference (MMI) devices. In this paper, we investigate the active control of these MMIs and their suitability for approximating traditionally used unitary circuits. 
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