As the volume of data recorded by embedded edge sensors increases, particularly from neuromorphic devices producing discrete event streams, there is a need for hardware-aware neural architectures that enable efficient, low-latency and energy-conscious local processing. To address this research gap, we present an FPGA i...
K. Jeziorek, Piotr Wzorek, Krzysztof Błachut et al.· ACM Transactions on Reconfig...· 0 citations
This report demonstrates using a detailed transaction-level model (TLM) of a probabilistic analogue in-memory computing (AIMC) processor that the same energy-based model can execute well over 1000x faster than data-center-grade hardware by eliminating the HBM interface and performing computation directly within on-chip...
Thomas Dalgaty, Eiji Kawasaki, M. de Prado et al.· 0 citations
A biologically grounded framework in which noisy neural and synaptic dynamics perform inference and learning via stochastic sampling from an internal energy function, capturing uncertainty over latent states and model parameters through neural and synaptic variability, respectively is discussed.
Thomas Dalgaty, Eiji Kawasaki, M. de Prado et al.· 1 citation
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