Study of Analog Processing Elements for Ai
il y a 4 semaines
**Study of analog processing elements for AI**:
- Réf
- **ABG-111720**
- Sujet de Thèse- 05/03/2023- Contrat doctoral- Université Clermont Auvergne- Lieu de travail- Clermont Ferrand - Auvergne-Rhône-Alpes - France- Intitulé du sujet- Study of analog processing elements for AI- Champs scientifiques- Electronique
- Informatique
- Mots clés- Artificial Intelligence, Analog Computing, Microelectronics, FPGA**Description du sujet**:
The ARTISTE project supported by the AURA region aims to provide solutions to the challenges of embedded computing for AI. Contextually, the scientific frontiers are therefore positioned in the field of "edge-computing". A part of this project is devoted to the advantages of analog processing for deep neural networks. Even if this approach may seem original, it is quite a good deal when considering energy efficiency. For this reason, the ambition of this project is to propose the design of an analog circuit dedicated to convolutional neural networks (CNN). Of course, it is not a question of making an integrated circuit dedicated to only one wired network. Thus, we propose the design of a kind of reprogrammable circuit including elementary analog processing units dedicated to CNNs.
The motivation of an analog based circuit lies in the limitations of digital electronics on the consumption aspects (W/MacOp) as well as on the computation range (limitation of the number of bits in the arithmetic). The idea that we are pushing in this project is to factorize most of the operations used in deep learning to a small set of atomic operations such as weighted multiplication, nonlinearity, multi-bit addition,... Most of these operations can be described very well in terms of basic functions of analog electronics. Thus, a result of this work is to design a mixed reconfigurable circuit conceptually close to FPGA circuits but where the elementary components would be the above cited atomic operations. This mixed technology circuit would integrate a RISC V type processor for the internal control and a "sea" of elementary analog blocks that can be interconnected by programming.
With this study, we would like to reconsider the possibilities of analog calculation. Attempts have already been proposed in the literature [1][2][3][4] but this remains marginal in comparison with digital circuits. Concretely, we have identified three main challenges:
1 - The functional formalization of the analog or mixed computation units and the architecture of the system. The PhD student will have to make its microelectronic design.
2 - The problem of storing the network weights. Attempts have been made in analog design but remain in most cases associated with strong technological constraints
3 - The study of noise and bandwidth limitation induced by the cascading of analog computing units. Indeed, in the present case, it is reasonable to assume the circuit as a "pipelined data flow" graph. If this structure is classical in digital, it has a totally different nature in analog electronics.
References:
[1] Qiuwen Lou, Chenyun Pan, John McGuinness, Andras Horvath, Azad Naeemi, Michael Niemier, and X. Sharon Hu. 2019. A Mixed Signal Architecture for Convolutional Neural Networks. J. Emerg. Technol. Comput. Syst. 15, 2, Article 19 (June 2019).
[2]T. Patrick Xiao, Christopher H. Bennett, Ben Feinberg, Sapan Agarwal, and Matthew J. Marinella
[3]A. Shafiee et al, "ISAAC: A Convolutional Neural Network Accelerator with In-Situ Analog Arithmetic in Crossbars," 2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA), 2016, pp. 14-26, doi: 10.1109/ISCA.2016.12.
[4]Matthew Z. Wong, Benoît Guillard, Riku Murai, Sajad Saeedi, Paul H. J. Kelly: AnalogNet: Convolutional Neural Network Inference on Analog Focal Plane Sensor Processors. CoRR abs/2006.01765 (2020)
**Prise de fonction**:
- 01/06/2026**Nature du financement**:
- Contrat doctoral**Précisions sur le financement**:
**Présentation établissement et labo d'accueil**:
- Université Clermont AuvergneClermont Auvergne University (French: Université Clermont Auvergne) is a public research university with its main campus in Clermont-Ferrand, France. It was created with the merger of Blaise Pascal University and the University of Auvergne on 1 January 2017.
Clermont Auvergne University comprises 22 components, divided into Training and Research Units (UFR, formerly faculties), Schools and Institutes (IUT).
**Site web**:
**Intitulé du doctorat**:
- Doctorat en electronique**Pays d'obtention du doctorat**:
- France**Ecole doctorale**:
- ED SPI - UCAAnalog electronics or microelectronics (required), integrated circuit design (optional). Knowledge in AI would be a plus but not a major selection criterion.- 31/05/2023
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