Origami: A 803 GOp/s/W Convolutional Network Accelerator

2015-12-14 · Lukas Cavigelli, Luca Benini

An ever increasing number of computer vision and image/video processing challenges are being approached using deep convolutional neural networks, obtaining state-of-the-art results in object recognition and detection, semantic segmentation, action recognition, optical flow and superresolution. Hardware acceleration of these algorithms is essential to adopt these improvements in embedded and mobile computer vision systems. We present a new architecture, design and implementation as well as the first reported silicon measurements of such an accelerator, outperforming previous work in terms of power-, area- and I/O-efficiency. The manufactured device provides up to 196 GOp/s on 3.09 mm^2 of silicon in UMC 65nm technology and can achieve a power efficiency of 803 GOp/s/W. The massively reduced bandwidth requirements make it the first architecture scalable to TOp/s performance.

📄 PDF Abstract BibTeX arXiv:1512.04295

Code (0)

등록된 구현이 없습니다.

Tasks

Action Recognition Object Recognition Optical Flow Estimation Semantic Segmentation Temporal Action Localization

ORIGAMI: A Heterogeneous Split Architecture for In-Memory Acceleration of Learning

2018-12-30 · Hajar Falahati, Pejman Lotfi-Kamran, Mohammad Sadrosadati, Hamid Sarbazi-Azad

Memory bandwidth bottleneck is a major challenges in processing machine learning (ML) algorithms. In-memory acceleration has potential to address this problem; however, it needs to address two challenges. First, in-memor…

GPU

Privacy-Preserving Inference in Machine Learning Services Using Trusted Execution Environments

2019-12-07 · Krishna Giri Narra, Zhifeng Lin, Yongqin Wang, Keshav Balasubramaniam 외

This work presents Origami, which provides privacy-preserving inference for large deep neural network (DNN) models through a combination of enclave execution, cryptographic blinding, interspersed with accelerator-based c…

BIG-bench Machine Learning CPU Denoising GPU+1

ORIGAMI: A generative transformer architecture for predictions from semi-structured data

2024-12-23 · Thomas Rückstieß, Alana Huang, Robin Vujanic

Despite the popularity and widespread use of semi-structured data formats such as JSON, end-to-end supervised learning applied directly to such data remains underexplored. We present ORIGAMI (Object RepresentatIon via Ge…

Code Classification Multi-Label Classification MUlTI-LABEL-ClASSIFICATION valid

OrigamiSet1.0: Two New Datasets for Origami Classification and Difficulty Estimation

2021-01-14 · Daniel Ma, Gerald Friedland, Mario Michael Krell

Origami is becoming more and more relevant to research. However, there is no public dataset yet available and there hasn't been any research on this topic in machine learning. We constructed an origami dataset using imag…

BIG-bench Machine Learning Classification General Classification Vocal Bursts Valence Prediction

Harnessing Interpretable Machine Learning for Holistic Inverse Design of Origami

2022-04-12 · Yi Zhu, Evgueni T. Filipov

This work harnesses interpretable machine learning methods to address the challenging inverse design problem of origami-inspired systems. We show that a decision tree-random forest method is particularly suitable for fit…

BIG-bench Machine Learning Interpretable Machine Learning