In this paper, we provide an in-depth analysis of the computation, energy and accuracy trade-offs between learned features such as deep Convolutional Neural ...

Mar 17, 2017 · In this paper, we provide an in-depth analysis of the computation, energy and accuracy trade-offs between learned features such as deep ...

Towards Closing the Energy Gap Between HOG and CNN Features for Embedded Vision. Contact Info email: [email protected] website: www.rle.mit.edu/eems. Amr Suleiman ...

The goal is to understand the source of the energy discrepancy between the two approaches and to provide insight about the potential areas where CNNs can be ...

Mar 21, 2017 · In this paper, we provide an in-depth analysis of the computation, energy and accuracy trade-offs between learned features such as deep ...

Aug 13, 2018 · Bibliographic details on Towards Closing the Energy Gap Between HOG and CNN Features for Embedded Vision.

Aug 1, 2022 · Amr Suleiman, Yu-Hsin Chen, Joel Emer, and Vivienne Sze. 2017. Towards closing the energy gap between HOG and CNN features for embedded vision.

Eyeriss is an energy-efficient deep convolutional neural network (CNN) accelerator that supports state-of-the-art CNNs.

Apr 21, 2023 · Towards Closing the Energy Gap Between HOG and CNN Features for Embedded Vision. MIT EEMS Group - PI: Vivienne Sze · 56:35 · "Approaches for ...

Towards Closing the Energy Gap Between HOG and CNN Features for Embedded Vision ... Computer vision enables a wide range of applications in robotics/drones ...