Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Bert Moons"'
Publikováno v:
IEEE Journal of Solid-State Circuits. 54:158-172
The trend of pushing inference from cloud to edge due to concerns of latency, bandwidth, and privacy has created demand for energy-efficient neural network hardware. This paper presents a mixed-signal binary convolutional neural network (CNN) process
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1555cd48d49637fea975388149c7c75d
https://doi.org/10.1109/jssc.2018.2869150
https://doi.org/10.1109/jssc.2018.2869150
Autor:
Bert Moons, Marian Verhelst
Publikováno v:
System-Scenario-based Design Principles and Applications ISBN: 9783030203429
Dynamic-voltage-accuracy-frequency-scaling (DVAFS) is a generalization of the more common concept of dynamic-voltage-frequency-scaling (DVFS) where, instead of modulating voltage and frequency with changing throughput requirements, voltage and freque
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8f750bce536f46fd85e24ea3d085e566
https://doi.org/10.1007/978-3-030-20343-6_5
https://doi.org/10.1007/978-3-030-20343-6_5
Publikováno v:
Embedded Deep Learning ISBN: 9783319992228
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::122c30fa6c0cd5bca044e6f09dd3ff43
https://doi.org/10.1007/978-3-319-99223-5_2
https://doi.org/10.1007/978-3-319-99223-5_2
Publikováno v:
Embedded Deep Learning ISBN: 9783319992228
Deep learning networks have recently come up as the state-of-the-art classification algorithms in artificial intelligence, achieving super-human performance in a number of perceptive tasks in computer vision and automated speech recognition. Although
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::960a4bbaf57d7d0bcc6fdd633e78499d
https://doi.org/10.1007/978-3-319-99223-5_1
https://doi.org/10.1007/978-3-319-99223-5_1
Publikováno v:
Embedded Deep Learning ISBN: 9783319992228
This dissertation has focused on techniques to minimize the energy consumption of deep learning algorithms for embedded applications on battery-constrained wearable edge devices. Although SotA in many typical machine-learning tasks, deep learning alg
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3b0fe7e85b5b96ba4846ab887eda63f1
https://doi.org/10.1007/978-3-319-99223-5_7
https://doi.org/10.1007/978-3-319-99223-5_7
Publikováno v:
Embedded Deep Learning ISBN: 9783319992228
As discussed in Chap. 1, neural network-based applications are still too costly for them to be embedded on mobile and always-on devices. This chapter discusses hardware aware algorithm-level solutions for this problem. As an introduction to this topi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b0fc34d56f294ee830f03554d724409d
https://doi.org/10.1007/978-3-319-99223-5_3
https://doi.org/10.1007/978-3-319-99223-5_3
Publikováno v:
Embedded Deep Learning ISBN: 9783319992228
This chapter focuses on approximate computing (AC), a set of software- and primarily hardware-level techniques in which algorithm accuracy is traded for energy consumption by deliberately introducing acceptable errors into the computing process. It i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7c85e99edfbbb2a751ef1fc22fd9f856
https://doi.org/10.1007/978-3-319-99223-5_4
https://doi.org/10.1007/978-3-319-99223-5_4
Publikováno v:
Embedded Deep Learning ISBN: 9783319992228
The Envision CNN processors discussed in Chap. 5 are efficient but not sufficient for always-on embedded inference. Both neural networks and ASICs can be further optimized for such specific applications. To this end, this chapter focuses on two proto
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8f1671508d9fceba78b6f8b35a2852b9
https://doi.org/10.1007/978-3-319-99223-5_6
https://doi.org/10.1007/978-3-319-99223-5_6
Autor:
Rebecca Park, Bert Moons, Jake Hillard, Daniel Bankman, Gage Hills, Lita Yang, Max M. Shulaker, Boris Murmann, Marian Verhelst, Alex Kahng, Subhasish Mitra, H.-S. Philip Wong
Publikováno v:
DAC
The energy efficiency demands of future abundant-data applications, e.g., those which use inference-based techniques to classify large amounts of data, exceed the capabilities of digital systems today. Field-effect transistors (FETs) built using nano
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8df968ab8b6d1e94373ac2f1b2c9884a
https://doi.org/10.1145/3195970.3196132
https://doi.org/10.1145/3195970.3196132