Controlling Information and Privacy Loss in Machine Learning Models with Coarseness
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Outkin, Alexander V.↗
Engineering topics
Dellana, Ryan Anthony
Publications and source records attributed to Dellana, Ryan Anthony.
Evaluating complexity and resilience trade-offs in emerging memory inference machines.
Abstract not provided.
Bennett, Christopher H↗
Mosaics , The Best of Both Worlds: Analog devices with Digital Spiking Communication to build a Hybrid Neural Network Accelerator
Neuromorphic architectures have seen a resurgence of interest in the past decade owing to 100x-1000x efficiency gain over conventional Von Neumann architectures. Digital neuromorphic chips like Intel's Loihi have shown efficiency gains compared to GPUs and CPUs and can be scaled to build larger systems. Analog neuromorphic architectures promise even further savings in energy efficiency, area, and latency than their digital counterparts. Neuromorphic analog and digital technologies provide both low-power and configurable acceleration of challenging artificial intelligence (AI) algorithms. We present a hybrid analog-digital neuromorphic architecture that can amplify the advantages of both high-density analog memory and spike-based digital communication while mitigating each of the other approaches' limitations.
97 MATHEMATICS AND COMPUTING↗