229 research outputs found
Développement d'architectures HW/SW tolérantes aux fautes et auto-calibrantes pour les technologies Intégrées 3D
Malgré les avantages de l'intégration 3D, le test, le rendement et la fiabilité des Through-Silicon-Vias (TSVs) restent parmi les plus grands défis pour les systèmes 3D à base de Réseaux-sur-Puce (Network-on-Chip - NoC). Dans cette thèse, une stratégie de test hors-ligne a été proposé pour les interconnections TSV des liens inter-die des NoCs 3D. Pour le TSV Interconnect Built-In Self-Test (TSV-IBIST) on propose une nouvelle stratégie pour générer des vecteurs de test qui permet la détection des fautes structuraux (open et short) et paramétriques (fautes de délaye). Des stratégies de correction des fautes transitoires et permanents sur les TSV sont aussi proposées aux plusieurs niveaux d'abstraction: data link et network. Au niveau data link, des techniques qui utilisent des codes de correction (ECC) et retransmission sont utilisées pour protégé les liens verticales. Des codes de correction sont aussi utilisés pour la protection au niveau network. Les défauts de fabrication ou vieillissement des TSVs sont réparé au niveau data link avec des stratégies à base de redondance et sérialisation. Dans le réseau, les liens inter-die défaillante ne sont pas utilisables et un algorithme de routage tolérant aux fautes est proposé. On peut implémenter des techniques de tolérance aux fautes sur plusieurs niveaux. Les résultats ont montré qu'une stratégie multi-level atteint des très hauts niveaux de fiabilité avec un cout plus bas. Malheureusement, il n'y as pas une solution unique et chaque stratégie a ses avantages et limitations. C'est très difficile d'évaluer tôt dans le design flow les couts et l'impact sur la performance. Donc, une méthodologie d'exploration de la résilience aux fautes est proposée pour les NoC 3D mesh.3D technology promises energy-efficient heterogeneous integrated systems, which may open the way to thousands cores chips. Silicon dies containing processing elements are stacked and connected by vertical wires called Through-Silicon-Vias. In 3D chips, interconnecting an increasing number of processing elements requires a scalable high-performance interconnect solution: the 3D Network-on-Chip. Despite the advantages of 3D integration, testing, reliability and yield remain the major challenges for 3D NoC-based systems. In this thesis, the TSV interconnect test issue is addressed by an off-line Interconnect Built-In Self-Test (IBIST) strategy that detects both structural (i.e. opens, shorts) and parametric faults (i.e. delays and delay due to crosstalk). The IBIST circuitry implements a novel algorithm based on the aggressor-victim scenario and alleviates limitations of existing strategies. The proposed Kth-aggressor fault (KAF) model assumes that the aggressors of a victim TSV are neighboring wires within a distance given by the aggressor order K. Using this model, TSV interconnect tests of inter-die 3D NoC links may be performed for different aggressor order, reducing test times and circuitry complexity. In 3D NoCs, TSV permanent and transient faults can be mitigated at different abstraction levels. In this thesis, several error resilience schemes are proposed at data link and network levels. For transient faults, 3D NoC links can be protected using error correction codes (ECC) and retransmission schemes using error detection (Automatic Retransmission Query) and correction codes (i.e. Hybrid error correction and retransmission).For transients along a source-destination path, ECC codes can be implemented at network level (i.e. Network-level Forward Error Correction). Data link solutions also include TSV repair schemes for faults due to fabrication processes (i.e. TSV-Spare-and-Replace and Configurable Serial Links) and aging (i.e. Interconnect Built-In Self-Repair and Adaptive Serialization) defects. At network-level, the faulty inter-die links of 3D mesh NoCs are repaired by implementing a TSV fault-tolerant routing algorithm. Although single-level solutions can achieve the desired yield / reliability targets, error mitigation can be realized by a combination of approaches at several abstraction levels. To this end, multi-level error resilience strategies have been proposed. Experimental results show that there are cases where this multi-layer strategy pays-off both in terms of cost and performance. Unfortunately, one-fits-all solution does not exist, as each strategy has its advantages and limitations. For system designers, it is very difficult to assess early in the design stages the costs and the impact on performance of error resilience. Therefore, an error resilience exploration (ERX) methodology is proposed for 3D NoCs.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF
New Perspectives on Core In-field Path Delay Test
Path Delay fault test currently exploits DfT-based techniques, mainly relying on scan chains, widely supported by commercial tools. However, functional testing may be a desirable choice in this context because it allows to catch faults at-speed with no hardware overhead and it can be used both for endof-manufacturing tests and for in-field test. The purpose of this article is to compare the results that can be achieved with both approaches. This work is based on an open-source RISC-V-based processor core as benchmark device. Gathered results show that there is no correlation between stuck-at and path delay fault coverage, and provide guidelines for developing more effective functional test
Spatial-SpinDrop: Spatial Dropout-based Binary Bayesian Neural Network with Spintronics Implementation
Recently, machine learning systems have gained prominence in real-time,
critical decision-making domains, such as autonomous driving and industrial
automation. Their implementations should avoid overconfident predictions
through uncertainty estimation. Bayesian Neural Networks (BayNNs) are
principled methods for estimating predictive uncertainty. However, their
computational costs and power consumption hinder their widespread deployment in
edge AI. Utilizing Dropout as an approximation of the posterior distribution,
binarizing the parameters of BayNNs, and further to that implementing them in
spintronics-based computation-in-memory (CiM) hardware arrays provide can be a
viable solution. However, designing hardware Dropout modules for convolutional
neural network (CNN) topologies is challenging and expensive, as they may
require numerous Dropout modules and need to use spatial information to drop
certain elements. In this paper, we introduce MC-SpatialDropout, a spatial
dropout-based approximate BayNNs with spintronics emerging devices. Our method
utilizes the inherent stochasticity of spintronic devices for efficient
implementation of the spatial dropout module compared to existing
implementations. Furthermore, the number of dropout modules per network layer
is reduced by a factor of and energy consumption by a factor of
, while still achieving comparable predictive performance and
uncertainty estimates compared to related works
Logic synthesis and testing techniques for switching nano-crossbar arrays
Beyond CMOS, new technologies are emerging to extend electronic systems with features unavailable to silicon-based devices. Emerging technologies provide new logic and interconnection structures for computation, storage and communication that may require new design paradigms, and therefore trigger the development of a new generation of design automation tools. In the last decade, several emerging technologies have been proposed and the time has come for studying new ad-hoc techniques and tools for logic synthesis, physical design and testing. The main goal of this project is developing a complete synthesis and optimization methodology for switching nano-crossbar arrays that leads to the design and construction of an emerging nanocomputer. New models for diode, FET, and four-terminal switch based nanoarrays are developed. The proposed methodology implements logic, arithmetic, and memory elements by considering performance parameters such as area, delay, power dissipation, and reliability. With combination of logic, arithmetic, and memory elements a synchronous state machine (SSM), representation of a computer, is realized. The proposed methodology targets variety of emerging technologies including nanowire/nanotube crossbar arrays, magnetic switch-based structures, and crossbar memories. The results of this project will be a foundation of nano-crossbar based circuit design techniques and greatly contribute to the construction of emerging computers beyond CMOS. The topic of this project can be considered under the research area of â\u80\u9cEmerging Computing Modelsâ\u80\u9d or â\u80\u9cComputational Nanoelectronicsâ\u80\u9d, more specifically the design, modeling, and simulation of new nanoscale switches beyond CMOS
Unbiased Random Number Generation using Injection-Locked Spin-Torque Nano-Oscillators
Unbiased sources of true randomness are critical for the successful
deployment of stochastic unconventional computing schemes and encryption
applications in hardware. Leveraging nanoscale thermal magnetization
fluctuations provides an efficient and almost cost-free means of generating
truly random bitstreams, distinguishing them from predictable pseudo-random
sequences. However, existing approaches that aim to achieve randomness often
suffer from bias, leading to significant deviations from equal fractions of 0
and 1 in the bitstreams and compromising their inherent unpredictability. This
study presents a hardware approach that capitalizes on the intrinsic balance of
phase noise in an oscillator injection locked at twice its natural frequency,
leveraging the stability of this naturally balanced physical system. We
demonstrate the successful generation of unbiased and truly random bitstreams
through extensive experimentation. Our numerical simulations exhibit excellent
agreement with the experimental results, confirming the robustness and
viability of our approach.Comment: 13 pages, 8 figure
Integrated Synthesis Methodology for Crossbar Arrays
Nano-crossbar arrays have emerged as area and power efficient
structures with an aim of achieving high performance computing
beyond the limits of current CMOS. Due to the stochastic nature
of nano-fabrication, nano arrays show different properties both
in structural and physical device levels compared to conventional
technologies. Mentioned factors introduce random characteristics
that need to be carefully considered by synthesis process. For instance, a competent synthesis methodology must consider basic
technology preference for switching elements, defect or fault rates
of the given nano switching array and the variation values as well
as their effects on performance metrics including power, delay, and
area. Presented synthesis methodology in this study comprehensively covers the all specified factors and provides optimization
algorithms for each step of the process.This work is part of a project that has received funding from the
European Union’s H2020 research and innovation programme under the
Marie Skłodowska-Curie grant agreement No 691178, and supported by the
TUBITAK-Career project #113E76
Ramucirumab plus docetaxel versus placebo plus docetaxel in patients with locally advanced or metastatic urothelial carcinoma after platinum-based therapy (RANGE): a randomised, double-blind, phase 3 trial
Few treatments with a distinct mechanism of action are available for patients with platinum-refractory advanced or metastatic urothelial carcinoma. We assessed the efficacy and safety of treatment with docetaxel plus either ramucirumab-a human IgG1 VEGFR-2 antagonist-or placebo in this patient population
Moniteurs de fiabilité embarqués en technologie FDSOI: Implémentation et Applications
International audienc
Run-time Age Induced Reliability Prediction for SOC
International audienc
- …