Étude des mémoires non volatiles émergentes à plusieurs niveaux pour le calcul en mémoire et les réseaux neuronaux analogiques (SNN) basés sur la technologie FD-SOI
Notice bibliographique
Résumé
The ever-increasing computational demand, the advent of artificial intelligence (AI), and the bottleneck of the traditional CMOS-based Von Neumann architecture has raised a debate concerning energy sustainability. To circumvent these challenges, it is paramount to seek for alternative hardware implementations. Examples of energy efficient unconventional computing solutions are brain-inspired systems including spiking neural networks (SNNs). Traditionally, the prevailing computing paradigm has involved the transmission of continuous floating variables from one processing unit to another. However, insights from neurobiology and brain-inspired computing underscore that SNN communicate through discrete pulses. As a fundamental building block component for such hardware implementations, emerging non-volatile memories (eNVMs) stand out as promising memory component, which outperform and exceeds complementary metal-oxide-semiconductor (CMOS) based technologies in processing and non-volatile storage capabilities. Yet, it inherently suggests the colocation of memory and processing units in in-memory computing hardware solutions. To implement it in hardware, different solutions of eNVMs are investigated and benchmarked throughout the thesis. In chapter 3, As a case study, we analyze the memory solutions fabricated in the laboratories associated with this project. These solutions serve as a practical example to assess the efficacy and performance of different analog eNVM technologies. The solutions are Phase change memories (PCM) provided by STMicroelectronics, titanium oxide (TiOx) based resistive memories (OxRAM) from Institut interdisciplinaire d'innovation technologique (3IT) and spin transfer torque (STT) and spin orbit torque (SOT) magnetic random-access memories (MRAM) from Spintronique et Technologie des Composants (SPINTEC). Experimental characterization is conducted on PCM and TiO2 OxRAM, while data regarding SOT-MRAM is sourced from simulations or provided by the SPINTEC IC design team. The methodology employed to perform the electrical characterization and analog programming are depicted. The PCM, OxRAM and SOT-MRAM give rises to 44, 10, and 5 3IT multilevel states respectively. Nonidealities aspects such as variability are also included in the analysis. The operation requirements are considered to further co-integrate these eNVMs into a 28 nm Fully Depleted Silicon On Insulator (FD-SOI) based neuron solution designed, tested, and depicted in chapter 4. A co-design methodology to co-integrate and implement in hardware eNVMs with FD-SOI based fully analog neurons is provided and a multi-project work (MPW) comprising an analog neuron, a current attenuator, and selectors for memory integration is deployed. According to test the analog neuron consumed 3.86 pJ/spike. Finally, the multilevel and drift behaviour of 1 Transistor - 1 Resistor (1T1R) PCM are exhaustively explored at cryogenic environments in chapter 5. The 1T1R PCMs are fully characterized at 300 K, 77 K and 12 K. The ePCM multilevel capabilities give rise to 10 multilevel states at 77 K and 12 K while 7 states at 300 K. The performance and effect of non-idealities at different temperatures are modelled and evaluated in SNN Mixed National Institute of Standards and Technology (MNIST) classification task. The SNN classification accuracy is sustained up to 2 years at 77 K and 12 K while a 12 % drop in accuracy is observed at 300 K. More importantly, without requiring any additional hardware or software solution for drift mitigation. In addition, a hardware and operation solution based on non-linear current scaling are proposed to mitigate the non-ideality aspects of 1T1R PCMs at room temperature, the coefficient of variability and the drift is reduced leading to a sustain and improvement of accuracy in a SNN MNIST classification task. The variability is reduced by up to 5 % and the drift is compensated for years.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».