Heterogeneous computing platforms for resource-aware video and data analytics (HOPWARE)
Lead Research Organisation:
University of Bristol
Department Name: Electrical and Electronic Engineering
Abstract
HOPWARE will investigate how the multi-objective platform created in the CERBERO H2020 project (https://www.cerbero-h2020.eu/) can be applied to heterogeneous computing platforms that combine deep-learning compute units with different capabilities, performance and power characteristics. The proposed heterogeneous computing platform in HOPWARE combines devices like Google EdgeTPUs, Intel VPUs, ARM processors and Xilinx FPGAs programmable via a single machine-learning framework like TensorFlow so that once training completes the network can be run in an optimal configuration of multiple devices. Project partner UPM created in CERBERO an adaptive framework called ARTICO that can assess the status of the system at run-time and deploy different reconfiguration strategies to improve its energy efficiency, robustness and performance. In this research we are particularly interested in the ARTICO/CERBERO approach to run-time monitoring, sensing and estimation of capabilities to assist in the decision of how to distribute the available resources to achieve a functional goal within constraints in power and execution time.
People |
ORCID iD |
| Jose Nunez-Yanez (Principal Investigator) |
Publications
Nunez-Yanez J
(2021)
Sparse and dense matrix multiplication hardware for heterogeneous multi-precision neural networks
in Array
| Description | A hardware accelerator working with sparse matrix can be used long a dense accelerator and different threshold points can be identified to switch between both modes depending on the classification task complexity resulting in significant energy savings. Dynamic function exchange can be used to extend this to precisions involving floating point and fixed point numbers. |
| Exploitation Route | Further funding to explore how this can be combined with dynamic function exchange or partial reconfiguration as developed by academic partner UPM has been obtained from the Leverhulme trust. |
| Sectors | Aerospace, Defence and Marine,Electronics |
| Description | "High-performance video analytics with parallel heterogeneous neural networks |
| Amount | £37,000 (GBP) |
| Funding ID | IF-2021-003 |
| Organisation | The Leverhulme Trust |
| Sector | Charity/Non Profit |
| Country | United Kingdom |
| Start | 01/2022 |
| End | 06/2022 |
| Description | international collaboration Universidad Politecnica De Madrid |
| Organisation | Technical University of Madrid |
| Country | Spain |
| Sector | Academic/University |
| PI Contribution | Edge processor optimized for sparse/dense tensor computing in Tensorflow Lite. |
| Collaborator Contribution | Use of facilities including computer systems, office space and dynamic reconfiguration technology part of the CERBERO EU project. |
| Impact | Nunez-Yanez, J. L., & Hosseinabady, M. (2021). Sparse and dense matrix multiplication hardware for heterogeneous multi-precision neural networks. Array, 12, [100101]. https://doi.org/10.1016/j.array.2021.100101 |
| Start Year | 2021 |