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  1. Nov 4, 2024To address this problem, we propose a new approach to embedded intelligence, called Fast-Inf, which achieves extremely lightweight computation and minimal latency. Fast-Inf uses binary tree-based neural networks that are ultra-fast and energy-efficient due to their logarithmic time complexity.
    • Fast-Inf: Ultra-Fast Embedded Intelligence on the Batteryless Edge

      in the small memory of batteryless devices. (2) Ultra-fast tiny inference. Running Fast-Inf models is ultra-fast and energy-efficient due to their logarithmic time complexity. We observed up to approximately 700×speedup and less energy consumption compared to DNNs during our experiments on the MSP430FR5994 MCU. (3) Tiny runtime.

  2. Nov 4, 2024in the small memory of batteryless devices. (2) Ultra-fast tiny inference. Running Fast-Inf models is ultra-fast and energy-efficient due to their logarithmic time complexity. We observed up to approximately 700×speedup and less energy consumption compared to DNNs during our experiments on the MSP430FR5994 MCU. (3) Tiny runtime.
  3. This is the repo associated to the paper Fast-Inf: Ultra-Fast Embedded Intelligence on the Batteryless Edge, to be published at ACM SenSys 2024. To reproduce the experiments, please run: python fff_experiment_mnist.py < leaf_width > < depth > < epochs > < norm_weight >
  4. semanticscholar.org

    This work proposes a new approach to embedded intelligence, called Fast-Inf, which achieves extremely lightweight computation and minimal latency, and uses binary tree-based neural networks that are ultra-fast and energy-efficient due to their logarithmic time complexity. Batteryless edge devices are extremely resource-constrained compared to traditional mobile platforms.
  5. iris.unitn.it

    Jan 1, 2024Existing tiny deep neural network (DNN) inference solutions are problematic due to their slow and resource-intensive nature, rendering them unsuitable for batteryless edge devices. To address this problem, we propose a new approach to embedded intelligence, called Fast-Inf, which achieves extremely lightweight computation and minimal latency.
  6. researchgate.net

    Nov 4, 2024Request PDF | On Nov 4, 2024, Leonardo Lucio Custode and others published Fast-Inf: Ultra-Fast Embedded Intelligence on the Batteryless Edge | Find, read and cite all the research you need on ...
  7. sinanyil81.github.io

    sinanyil81.github.io

    https://sinanyil81.github.io

    Fast-Inf: Ultra-Fast Embedded Intelligence on the Batteryless Edge, Leonardo Custode, Farina, Pietro, Eren Yildiz, Renan Beran Kilic, Kasim Sinan Yildirim, Giovanni Iacca SenSys'24 Memory-efficient Energy-adaptive Inference of Pre-Trained Models on Batteryless Embedded Systems , Farina, Pietro, Biswas, Subrata, Yildiz, Eren, Akhunov, Khakim ...
  8. erenyildiz33.github.io

    Publications. Fast-Inf: Ultra-Fast Embedded Intelligence on the Batteryless Edge, SenSys 2024.; Memory-efficient Energy-adaptive Inference of Pre-Trained Models on Batteryless Embedded Systems, EWSN 2024.; On Tracking Time with Better Resolution and Range in Batteryless Systems, ENSsys 2024.; Bootstrapping health wearables powered by Intra-Body Power Transfer, BSN 2024.
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