75 research outputs found

    Compact and High-Performance TCAM Based on Scaled Double-Gate FeFETs

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    Ternary content addressable memory (TCAM), widely used in network routers and high-associativity caches, is gaining popularity in machine learning and data-analytic applications. Ferroelectric FETs (FeFETs) are a promising candidate for implementing TCAM owing to their high ON/OFF ratio, non-volatility, and CMOS compatibility. However, conventional single-gate FeFETs (SG-FeFETs) suffer from relatively high write voltage, low endurance, potential read disturbance, and face scaling challenges. Recently, a double-gate FeFET (DG-FeFET) has been proposed and outperforms SG-FeFETs in many aspects. This paper investigates TCAM design challenges specific to DG-FeFETs and introduces a novel 1.5T1Fe TCAM design based on DG-FeFETs. A 2-step search with early termination is employed to reduce the cell area and improve energy efficiency. A shared driver design is proposed to reduce the peripherals area. Detailed analysis and SPICE simulation show that the 1.5T1Fe DG-TCAM leads to superior search speed and energy efficiency. The 1.5T1Fe TCAM design can also be built with SG-FeFETs, which achieve search latency and energy improvement compared with 2FeFET TCAM.Comment: Accepted by Design Automation Conference (DAC) 202

    HW/SW Codesign for Robust and Efficient Binarized SNNs by Capacitor Minimization

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    Using accelerators based on analog computing is an efficient way to process the immensely large workloads in Neural Networks (NNs). One example of an analog computing scheme for NNs is Integrate-and-Fire (IF) Spiking Neural Networks (SNNs). However, to achieve high inference accuracy in IF-SNNs, the analog hardware needs to represent current-based multiply-accumulate (MAC) levels as spike times, for which a large membrane capacitor needs to be charged for a certain amount of time. A large capacitor results in high energy use, considerable area cost, and long latency, constituting one of the major bottlenecks in analog IF-SNN implementations. In this work, we propose a HW/SW Codesign method, called CapMin, for capacitor size minimization in analog computing IF-SNNs. CapMin minimizes the capacitor size by reducing the number of spike times needed for accurate operation of the HW, based on the absolute frequency of MAC level occurrences in the SW. To increase the operation of IF-SNNs to current variation, we propose the method CapMin-V, which trades capacitor size for protection based on the reduced capacitor size found in CapMin. In our experiments, CapMin achieves more than a 14×\times reduction in capacitor size over the state of the art, while CapMin-V achieves increased variation tolerance in the IF-SNN operation, requiring only a small increase in capacitor size.Comment: 9 pages, 9 figure

    Actinomycosis in a gray four-eyed opossum (Philander opossum) caused by a novel species of Schaalia

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    Background: Infective lesions of the jaws and adjacent tissues (lumpy jaw disease, LJD) have been recognized as one major cause of death of captive macropods. Fusobacterium necrophorum and Actinomyces species serve as the main source of LJD in kangaroos and wallabies. Currently, little is reported about LJD or similar diseases in opossums. Case presentation: Here we report a case of actinomycosis resembling the entity lumpy jaw disease in a gray four-eyed opossum, caused by a novel species of Schaalia. A 2.8 year old male Philander opossum was presented with unilateral swelling of the right mandible. After an initial treatment with marbofloxacin, the opossum was found dead the following day and the carcass was submitted for necropsy. Postmortem examination revealed severe mandibular skin and underlying soft tissue infection with subsequent septicemia as the cause of death. Histological examination demonstrated Splendore-Hoeppli phenomenon, typically seen in classical cases of actinomycosis. Bacteriology of liver and mandibular mass yielded a previously undescribed species of Schaalia, whose 16 S rRNA gene sequence was 97.0 % identical to Schaalia canis. Whole genome sequencing of the opossum isolate and calculation of average nucleotide identity confirmed a novel species of Schaalia, for which no whole genome sequence is yet available. Conclusions: The herewith reported Schaalia infection in the gray four-eyed opossum resembling classical actinomycosis gives a novel insight into new exotic animal bacterial diseases. Schaalia species may belong to the normal oral microbiome, as in macropods, and may serve as a contributor to opportunistic infections. Due to the lack of current literature, more insights and improved knowledge about Schaalia spp. and their pathogenicity will be useful to choose appropriate therapy regimens and improve the treatment success rate and outcome in exotic and endangered species

    A pedestrian approach to the invariant Gibbs measures for the 2-d defocusing nonlinear Schrödinger equations

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    42 pagesInternational audienceWe consider the defocusing nonlinear Schr\"odinger equations on the two-dimensional compact Riemannian manifold without boundary or a bounded domain in R2\R^2. Our aim is to give a pedagogic and self-contained presentation on the Wick renormalization in terms of the Hermite polynomials and the Laguerre polynomials and construct the Gibbs measures corresponding to the Wick ordered Hamiltonian. Then, we construct global-in-time solutions with initial data distributed according to the Gibbs measure and show that the law of the random solutions, at any time, is again given by the Gibbs measure

    Finishing the euchromatic sequence of the human genome

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    The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∌99% of the euchromatic genome and is accurate to an error rate of ∌1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

    HW/SW Co-design for Reliable In-memory Brain-inspired Hyperdimensional Computing

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    Brain-inspired hyperdimensional computing (HDC) is continuously gaining remarkable attention. It is a promising alternative to traditional machine-learning approaches due to its ability to learn from little data, lightweight implementation, and resiliency against errors. However, HDC is overwhelmingly data-centric similar to traditional machine-learning algorithms. In-memory computing is rapidly emerging to overcome the von Neumann bottleneck by eliminating data movements between compute and storage units. In this work, we investigate and model the impact of imprecise in-memory computing hardware on the inference accuracy of HDC. Our modeling is based on 14nm FinFET technology fully calibrated with Intel measurement data. We accurately model, for the first time, the voltage-dependent error probability in SRAM-based and FeFET-based in-memory computing. Thanks to HDC's resiliency against errors, the complexity of the underlying hardware can be reduced, providing large energy savings of up to 6x. Experimental results for SRAM reveal that variability-induced errors have a probability of up to 39 percent. Despite such a high error probability, the inference accuracy is only marginally impacted. This opens doors to explore new tradeoffs. We also demonstrate that the resiliency against errors is application-dependent. In addition, we investigate the robustness of HDC against errors when the underlying in-memory hardware is realized using emerging non-volatile FeFET devices instead of mature CMOS-based SRAMs. We demonstrate that inference accuracy does remain high despite the larger error probability, while large area and power savings can be obtained. All in all, HW/SW co-design is the key for efficient yet reliable in-memory hyperdimensional computing for both conventional CMOS technology and upcoming emerging technologies.Comment: 12 pages, 16 figure

    Cryogenic In-Memory Computing for Quantum Processors Using Commercial 5-nm FinFETs

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    Cryogenic CMOS circuits that efficiently connect the classical domain with the quantum world are the cornerstone in bringing large-scale quantum processors to reality. The major challenges are, however, the tight power budget (in the order of milliwatts) and small latency (in the order of microseconds) requirements that such circuits inevitably must fulfill when operating at cryogenic temperatures. In-memory computing (IMC) is rapidly emerging as an attractive paradigm that holds the promise of performing computations efficiently where the data does not need to move back and forth between the CPU and the memory. Hence, it overcomes the fundamental bottleneck in classical von Neumann architectures, which provides considerable savings in power and latency. In this work, for the first time, we propose and implement an end-to-end approach that investigates SRAM-based IMC for cryogenic CMOS. To achieve that, we first characterize commercial 5 nm FinFETs from 300 K down to 10 K. Then, we employ the first cryogenic-aware industry-standard compact model for the FinFET technology (BSIM-CMG) to empower SPICE to accurately capture how cryogenic temperatures alter the electrical characteristics of transistors (e.g., threshold voltage, carrier mobility, sub-threshold slope, etc.). Our key contributions span from (1) carefully calibrating the cryogenic-aware BSIM-CMG against commercial 5 nm FinFET measurements in which SPICE simulations come with an excellent agreement with the experimental data, (2) exploring how the robustness of SRAM cells against noise (during the hold, read, and write operations) changes at extremely low temperatures, (3) investigating how the behavior of SRAM-based IMC circuits changes at 10 K compared to 300 K, and (4) modeling the error probabilities of IMC circuits that are used to calculate the Hamming distance, which is one of the essential similarity calculations to perform classifications

    An Artificial SEI Layer Based on an Inorganic Coordination Polymer with Self‐Healing Ability for Long‐Lived Rechargeable Lithium‐Metal Batteries

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    Upon immersion of a lithium (Li) anode into a diluted 0.05 to 0.20 M dimethoxyethanesolutionof the phosphoric acid derivative (CF 3 CH 2 O) 2 P(O)OH (HBFEP), anartificial solid electrolyte interphase (SEI) is generated on the Li-metal surface. Hence,HBFEP reacts on the surface to the corresponding Li salt (LiBFEP), which is a Li-ionconducting inorganic coordination polymer. This film exhibits -due to the reversiblybreaking ionic bonds- self-healing ability upon cycling-induced volume expansion of Li.The presence of LiBFEP as the major component in the artificial SEI is proven by ATRIRand XPS measurements. SEM characterization of HBFEP-treated Li samplesreveals porous layers on top of the Li surface with at least 3 ÎŒm thickness. Li-Lisymmetrical cells with HBFEP-modified Li electrodes show a three- to almost fourfoldcycle-lifetime increase at 0.1 mA·cm –2 in a demanding model electrolyte thatfacilitates fast battery failure (1 m LiOTf in TEGDME). Hence, the LiBFEP-enrichedlayer apparently acts as a Li-ion conducting protection barrier between Li and theelectrolyte, enhancing the rechargeability of Li electrodes
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