Software-defined Design Space Exploration for an Efficient DNN Accelerator Architecture

Deep neural networks (DNNs) have been shown to outperform conventional machine learning algorithms across a wide range of applications, e.g., image recognition, object detection, robotics, and natural language processing. However, the high computational complexity of DNNs often necessitates extremely fast and efficient hardware. The problem gets worse as the size of neural networks grows exponentially. As a result, customized hardware accelerators have been developed to accelerate DNN processing without sacrificing model accuracy. However, previous accelerator design studies have not fully considered the characteristics of the target applications, which may lead to sub-optimal architecture designs. On the other hand, new DNN models have been developed for better accuracy, but their compatibility with the underlying hardware accelerator is often overlooked. In this article, we propose an application-driven framework for architectural design space exploration of DNN accelerators. This framework is based on a hardware analytical model of individual DNN operations. It models the accelerator design task as a multi-dimensional optimization problem. We demonstrate that it can be efficaciously used in application-driven accelerator architecture design. Given a target DNN, the framework can generate efficient accelerator design solutions with optimized performance and area. Furthermore, we explore the opportunity to use the framework for accelerator configuration optimization under simultaneous diverse DNN applications. The framework is also capable of improving neural network models to best fit the underlying hardware resources

Expression analysis of heat-shock protein gene Hsp845 in the Antarctic psychrotrophic bacterium Psychrobacter sp. G under temperature and salinity stress

Heat shock proteins (Hsps), produced by organisms under high temperature stimulation, play important roles in protein folding, translocation, and refolding/degradation. In this study, we investigated the expression level of the GrpE Hsp gene Hsp845 of Psychrobacter sp. G under different temperature and salinity stresses by quantitative real-time PCR and western blotting, respectively. At both transcriptional and translational levels, Hsp845 gene expression was induced by high temperature (30°C) and inhibited by low temperatures (0°C and 10°C). Hsp845 expression was also induced both by the absence of salt (0‰) and high salinity (90‰ and 120‰) at the transcriptional level, but was only induced by high salinity (90‰ and 120‰) at the translational level. In a combined stress treatment, Hsp845 was more sensitive to high temperature than to salinity at both transcriptional and translational levels. The increase in the translational-level expression of Hsp845 lagged behind that at the transcriptional level, and Hsp845 maximum expression was also higher at the transcriptional than at the translational level. In the absence of salt, transcriptional- and translational-level expressions exhibited opposite patterns, suggesting that the underlying mechanism requires further study

Bacterial diversity in Arctic marine sediment determined by culture-dependent and -independent approaches

Bacterial diversity in surface sediment from the Arctic Ocean was investigated by culture-dependent and -independent approaches. Conventional culture-dependent techniques revealed 11 strains based on their distinct morphological characteristics on marine Zobell 2216E agar plates. Phylogenetic analysis showed that these isolates belonged to three major lineages of the Bacteria, γ-proteobacteria, Bacteroidetes and Actinobacteria, and that they included 10 genera. Most isolates were psychrotrophic, and NaCl was not necessary for their growth. Furthermore, they exhibited activity of at least one extracellular hydrolytic enzyme at 4°C and had various abilities to assimilate carbon sources. A total of 67 phylotypes were detected among 142 clones based on the 16S rRNA library of the total community DNA and grouped into nine major lineages of bacteria. Phylotypes affiliated with γ-, δ- and ε-proteobacteria accounted for 36.7%, 21.8% and 16.9% of the total clones, respectively. The rest of the clones belonged to Bacteroidetes, α-proteobacteria, Actinobacteria, Fusobacteria, Nitrospirae and an unclassified group

Charmed hadron chemistry in relativistic heavy-ion collisions

We develop for charmed hadron production in relativistic heavy-ion collisions a comprehensive coalescence model that includes an extensive set of $s$ and $p$-wave hadronic states as well as the strict energy-momentum conservation, which ensures the boost invariance of the coalescence probability and the thermal limit of the produced hadron spectrum. By combining our hadronization scheme with an advanced Langevin-hydrodynamics model that incorporates both elastic and inelastic energy loss of heavy quarks inside the dynamical quark-gluon plasma, we obtain a successful description of the $p_\mathrm{T}$-integrated and differential $\Lambda_c/D^0$ and $D_s/D^0$ ratios measured at RHIC and the LHC. We find that including the effect of radial flow of the medium is essential for describing the enhanced $\Lambda_c/D^0$ ratio observed in relativistic heavy-ion collisions. We also find that the puzzling larger $\Lambda_c/D^0$ ratio observed in Au+Au collisions at RHIC than in Pb+Pb collisions at the LHC is due to the interplay between the effects of the QGP radial flow and the charm quark transverse momentum spectrum at hadronization. Our study further suggests that charmed hadrons have larger sizes in medium than in vacuum.Comment: 6 pages, 5 figure

DeepSpeed-Chat: Easy, Fast and Affordable RLHF Training of ChatGPT-like Models at All Scales

ChatGPT-like models have revolutionized various applications in artificial intelligence, from summarization and coding to translation, matching or even surpassing human performance. However, the current landscape lacks an accessible, efficient, and cost-effective end-to-end RLHF (Reinforcement Learning with Human Feedback) training pipeline for these powerful models, particularly when training at the scale of billions of parameters. This paper introduces DeepSpeed-Chat, a novel system that democratizes RLHF training, making it accessible to the AI community. DeepSpeed-Chat offers three key capabilities: an easy-to-use training and inference experience for ChatGPT-like models, a DeepSpeed-RLHF pipeline that replicates the training pipeline from InstructGPT, and a robust DeepSpeed-RLHF system that combines various optimizations for training and inference in a unified way. The system delivers unparalleled efficiency and scalability, enabling training of models with hundreds of billions of parameters in record time and at a fraction of the cost. With this development, DeepSpeed-Chat paves the way for broader access to advanced RLHF training, even for data scientists with limited resources, thereby fostering innovation and further development in the field of AI.Comment: 14 pages, 7 figure