916 research outputs found
Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators
Analog in-memory computing (AIMC) -- a promising approach for
energy-efficient acceleration of deep learning workloads -- computes
matrix-vector multiplications (MVMs) but only approximately, due to
nonidealities that often are non-deterministic or nonlinear. This can adversely
impact the achievable deep neural network (DNN) inference accuracy as compared
to a conventional floating point (FP) implementation. While retraining has
previously been suggested to improve robustness, prior work has explored only a
few DNN topologies, using disparate and overly simplified AIMC hardware models.
Here, we use hardware-aware (HWA) training to systematically examine the
accuracy of AIMC for multiple common artificial intelligence (AI) workloads
across multiple DNN topologies, and investigate sensitivity and robustness to a
broad set of nonidealities. By introducing a new and highly realistic AIMC
crossbar-model, we improve significantly on earlier retraining approaches. We
show that many large-scale DNNs of various topologies, including convolutional
neural networks (CNNs), recurrent neural networks (RNNs), and transformers, can
in fact be successfully retrained to show iso-accuracy on AIMC. Our results
further suggest that AIMC nonidealities that add noise to the inputs or
outputs, not the weights, have the largest impact on DNN accuracy, and that
RNNs are particularly robust to all nonidealities.Comment: 35 pages, 7 figures, 5 table
"Le present est plein de l’avenir, et chargé du passé" : Vorträge des XI. Internationalen Leibniz-Kongresses, 31. Juli – 4. August 2023, Leibniz Universität Hannover, Deutschland. Band 3
[No abstract available]Deutschen Forschungsgemeinschaft (DFG)/Projektnr. 517991912VGH VersicherungNiedersächsisches Ministerium für Wissenschaft und Kultur (MWK
Using the IBM Analog In-Memory Hardware Acceleration Kit for Neural Network Training and Inference
Analog In-Memory Computing (AIMC) is a promising approach to reduce the
latency and energy consumption of Deep Neural Network (DNN) inference and
training. However, the noisy and non-linear device characteristics, and the
non-ideal peripheral circuitry in AIMC chips, require adapting DNNs to be
deployed on such hardware to achieve equivalent accuracy to digital computing.
In this tutorial, we provide a deep dive into how such adaptations can be
achieved and evaluated using the recently released IBM Analog Hardware
Acceleration Kit (AIHWKit), freely available at https://github.com/IBM/aihwkit.
The AIHWKit is a Python library that simulates inference and training of DNNs
using AIMC. We present an in-depth description of the AIHWKit design,
functionality, and best practices to properly perform inference and training.
We also present an overview of the Analog AI Cloud Composer, that provides the
benefits of using the AIHWKit simulation platform in a fully managed cloud
setting. Finally, we show examples on how users can expand and customize
AIHWKit for their own needs. This tutorial is accompanied by comprehensive
Jupyter Notebook code examples that can be run using AIHWKit, which can be
downloaded from https://github.com/IBM/aihwkit/tree/master/notebooks/tutorial
From Artificial Intelligence (AI) to Intelligence Augmentation (IA): Design Principles, Potential Risks, and Emerging Issues
We typically think of artificial intelligence (AI) as focusing on empowering machines with human capabilities so that they can function on their own, but, in truth, much of AI focuses on intelligence augmentation (IA), which is to augment human capabilities. We propose a framework for designing intelligent augmentation (IA) systems and it addresses six central questions about IA: why, what, who/whom, how, when, and where. To address the how aspect, we introduce four guiding principles: simplification, interpretability, human-centeredness, and ethics. The what aspect includes an IA architecture that goes beyond the direct interactions between humans and machines by introducing their indirect relationships through data and domain. The architecture also points to the directions for operationalizing the IA design simplification principle. We further identify some potential risks and emerging issues in IA design and development to suggest new questions for future IA research and to foster its positive impact on humanity
Atlas: Hybrid Cloud Migration Advisor for Interactive Microservices
Hybrid cloud provides an attractive solution to microservices for better
resource elasticity. A subset of application components can be offloaded from
the on-premises cluster to the cloud, where they can readily access additional
resources. However, the selection of this subset is challenging because of the
large number of possible combinations. A poor choice degrades the application
performance, disrupts the critical services, and increases the cost to the
extent of making the use of hybrid cloud unviable. This paper presents Atlas, a
hybrid cloud migration advisor. Atlas uses a data-driven approach to learn how
each user-facing API utilizes different components and their network footprints
to drive the migration decision. It learns to accelerate the discovery of
high-quality migration plans from millions and offers recommendations with
customizable trade-offs among three quality indicators: end-to-end latency of
user-facing APIs representing application performance, service availability,
and cloud hosting costs. Atlas continuously monitors the application even after
the migration for proactive recommendations. Our evaluation shows that Atlas
can achieve 21% better API performance (latency) and 11% cheaper cost with less
service disruption than widely used solutions.Comment: To appear at EuroSys 202
Bedford Springs Resort: A Political and Social Annex of Antebellum America: 1840-1860
Antebellum America has been described as a period of turbulence for the nation as the North and the South grew farther apart through sectionalism. While voters relied upon the increasing partisan press to inform them of debates in Washington and the often-deliberate decision to forgo the ultimate decision on slavery, in private politicians forged friendships through social events such as parties and dinners. When the Congressional session ended in early summer, politicians often accompanied by their families would travel north to Saratoga Springs or west to the much cooler climates of the mountain resorts: Bedford Springs, White Sulphur Springs, Warm Water Springs, or Berkeley Springs. Over the course of three to four weeks or the whole season, politicians would relax and enjoy the camaraderie of the company which included members of both the Northern and Southern elite. At Bedford Springs particularly, the resort provided the environment necessary for politicians of all parties to interact with members of the Northern and Southern gentry. Through their conversations and often annual meetings, local, state, and national politics were discussed and planned. As a result, the resort became synonymous with political patronage, especially that of James Buchanan. With a documented forty visits, the Pennsylvanian statesman made the resort his summer White House in 1857-1860, propelling Bedford Springs to the height of her popularity. Much like a spiderweb, the summers spent at Bedford Springs and its competitors not only strengthened the ties between members of the Northern and Southern elite but politicians as well. In contrast to the reporting of the partisan press, the discussions held at Bedford Springs between representatives of all parties were peaceful and productive, built on decades of friendship ignoring sectionalist differences
A quantum-resistant advanced metering infrastructure
This dissertation focuses on discussing and implementing a Quantum-Resistant Advanced
Metering Infrastructure (QR-AMI) that employs quantum-resistant asymmetric and symmetric
cryptographic schemes to withstand attacks from both quantum and classical computers. The
proposed solution involves the integration of Quantum-Resistant Dedicated Cryptographic
Modules (QR-DCMs) within Smart Meters (SMs). These QR-DCMs are designed to embed
quantum-resistant cryptographic schemes suitable for AMI applications. In this sense, it
investigates quantum-resistant asymmetric cryptographic schemes based on strong cryptographic
principles and a lightweight approach for AMIs. In addition, it examines the practical deployment
of quantum-resistant schemes in QR-AMIs. Two candidates from the National Institute of
Standards and Technology (NIST) post-quantum cryptography (PQC) standardization process,
FrodoKEM and CRYSTALS-Kyber, are assessed due to their adherence to strong cryptographic
principles and lightweight approach. The feasibility of embedding these schemes within QRDCMs in an AMI context is evaluated through software implementations on low-cost hardware,
such as microcontroller and processor, and hardware/software co-design implementations using
System-on-a-Chip (SoC) devices with Field-Programmable Gate Array (FPGA) components.
Experimental results show that the execution time for FrodoKEM and CRYSTALS-Kyber schemes
on SoC FPGA devices is at least one-third faster than software implementations. Furthermore, the
achieved execution time and resource usage demonstrate the viability of these schemes for AMI
applications. The CRYSTALS-Kyber scheme appears to be a superior choice in all scenarios,
except when strong cryptographic primitives are necessitated, at least theoretically. Due to the
lack of off-the-shelf SMs supporting quantum-resistant asymmetric cryptographic schemes, a QRDCM embedding quantum-resistant scheme is implemented and evaluated. Regarding hardware
selection for QR-DCMs, microcontrollers are preferable in situations requiring reduced processing
power, while SoC FPGA devices are better suited for those demanding high processing power.
The resource usage and execution time outcomes demonstrate the feasibility of implementing
AMI based on QR-DCMs (i.e., QR-AMI) using microcontrollers or SoC FPGA devices.Esta tese de doutorado foca na discussão e implementação de uma Infraestrutura de Medição
Avançada com Resistência Quântica (do inglês, Quantum-Resistant Advanced Metering Infrastructure - QR-AMI), que emprega esquemas criptográficos assimétricos e simétricos com
resistência quântica para suportar ataques proveniente tanto de computadores quânticos, como
clássicos. A solução proposta envolve a integração de um Módulo Criptográfico Dedicado
com Resistência Quântica (do inglês, Quantum-Resistant Dedicated Cryptographic Modules
- QR-DCMs) com Medidores Inteligentes (do inglĂŞs, Smart Meter - SM). Os QR-DCMs sĂŁo
projetados para embarcar esquemas criptográficos com resistência quântica adequados para
aplicação em AMI. Nesse sentido, é investigado esquemas criptográficos assimétricos com
resistĂŞncia quântica baseado em fortes princĂpios criptográficos e abordagem com baixo uso
de recursos para AMIs. Além disso, é analisado a implantação prática de um esquema com
resistência quântica em QR-AMIs. Dois candidatos do processo de padronização da criptografia
pós-quântica (do inglês, post-quantum cryptography - PQC) do Instituto Nacional de Padrões e
Tecnologia (do inglĂŞs, National Institute of Standards and Technology - NIST), FrodoKEM e
CRYSTALS-Kyber, sĂŁo avaliados devido Ă adesĂŁo a fortes princĂpios criptográficos e abordagem
com baixo uso de recursos. A viabilidade de embarcar esses esquemas em QR-DCMs em um
contexto de AMI é avaliado por meio de implementação em software em hardwares de baixo
custo, como um microcontrolador e processador, e implementações conjunta hardware/software
usando um sistema em um chip (do inglĂŞs, System-on-a-Chip - SoC) com Arranjo de Porta
Programável em Campo (do inglês, Field-Programmable Gate Array - FPGA). Resultados
experimentais mostram que o tempo de execução para os esquemas FrodoKEM e CRYSTALSKyber em dispositivos SoC FPGA é, ao menos, um terço mais rápido que implementações em
software. Além disso, os tempos de execuções atingidos e o uso de recursos demonstram a
viabilidade desses esquemas para aplicações em AMI. O esquema CRYSTALS-Kyber parece
ser uma escolha superior em todos os cenários, exceto quando fortes primitivas criptográficas
são necessárias, ao menos teoricamente. Devido à falta de SMs no mercado que suportem
esquemas criptográficos assimétricos com resistência quântica, um QR-DCM embarcando
esquemas com resistência quântica é implementado e avaliado. Quanto à escolha do hardware
para os QR-DCMs, microcontroladores sĂŁo preferĂveis em situações que requerem poder de
processamento reduzido, enquanto dispositivos SoC FPGA sĂŁo mais adequados para quando Ă©
demandado maior poder de processamento. O uso de recurso e o resultado do tempo de execução
demonstram a viabilidade da implementação de AMI baseada em QR-DCMs, ou seja, uma
QR-AMI, usando microcontroladores e dispositivos SoC FPGA
A Systematic Review of Intercultural Communication Competence Development in CEFR- Aligned English Proficiency Textbooks
The Common European Framework of Reference for Languages (CEFR) is a well-established outline that describes language learners’ abilities to use language and categorises what a learner can do using a six-point scale from basic users (A1) to proficient users (C2). CEFR offers a structure for developing language curriculum and syllabus, textbook, testing, and measuring and evaluating learning outcomes from kindergarten to tertiary levels (Little, 2016). As CEFR gains prominence within the curriculum and the global landscape becomes increasingly diverse, the question arises as to whether the learning resources in CEFR-aligned English textbooks adequately address ICC’s objectives and provide a comprehensive representation of cultural knowledge. Hence, the primary objective of this systematic review is to analyse current studies that investigate the incorporation of cultural material within English textbooks aligned with the CEFR framework, explicitly focusing on university-level students. The anticipated outcomes of this review are poised to provide a more lucid understanding of the prevailing theoretical and pedagogical challenges concerning integrating cultural elements into CEFR-aligned textbooks and ultimately seek to augment the level of ICC of university students
Language models in molecular discovery
The success of language models, especially transformer-based architectures,
has trickled into other domains giving rise to "scientific language models"
that operate on small molecules, proteins or polymers. In chemistry, language
models contribute to accelerating the molecule discovery cycle as evidenced by
promising recent findings in early-stage drug discovery. Here, we review the
role of language models in molecular discovery, underlining their strength in
de novo drug design, property prediction and reaction chemistry. We highlight
valuable open-source software assets thus lowering the entry barrier to the
field of scientific language modeling. Last, we sketch a vision for future
molecular design that combines a chatbot interface with access to computational
chemistry tools. Our contribution serves as a valuable resource for
researchers, chemists, and AI enthusiasts interested in understanding how
language models can and will be used to accelerate chemical discovery.Comment: Under revie
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