How to Stop Under-Utilization and Love Multicores

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The 70 kDa heat shock protein (Hsp70) chaperones control protein homeostasis in all ATP-containing cellular compartments. J-domain proteins (JDPs) coevolved with Hsp70s to trigger ATP hydrolysis and catalytically upload various substrate polypeptides in ne ...

Human babies have a natural desire to interact with new toys and objects, through which they learn how the world around them works, e.g., that glass shatters when dropped, but a rubber ball does not. When their predictions are proven incorrect, such as whe ...

The desire and ability to place AI-enabled applications on the edge has grown significantly in recent years. However, the compute-, area-, and power-constrained nature of edge devices are stressed by the needs of the AI-enabled applications, due to a gener ...

Traditional example-based learning methods are often limited by static, expert-created content. Hence, they face challenges in scalability, engagement, and effectiveness, as some learners might struggle to relate to the examples or find them relevant. To a ...

The increasing complexity of transformer models in artificial intelligence expands their computational costs, memory usage, and energy consumption. Hardware acceleration tackles the ensuing challenges by designing processors and accelerators tailored for t ...

This paper proposes a data-driven control design method for nonlinear systems that builds upon the Koopman operator framework. In particular, the Koopman operator is used to lift the nonlinear dynamics to a higher-dimensional space where the so-called obse ...

The invention relates to an Imaging sensor device in a stacked arrangement comprising: - a pixel array tier comprising a plurality of pixel segments each having a plurality of pixels for photon detection each providing a digital pixel output; - a processin ...

Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

Here we provide the neural data, activation and predictions for the best models and result dataframes of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the behavioral and neural experimental data (cu ...

A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...

The miniaturization of integrated circuits (ICs) and their higher performance and energy efficiency, combined with new machine learning algorithms and applications, have paved the way to intelligent, interconnected edge devices. In the medical domain, they ...

How to Stop Under-Utilization and Love Multicores

Chat with Graph Search

Autorepression of yeast Hsp70 cochaperones by intramolecular interactions involving their J-domains

Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

Exploring High-Performance and Energy-Efficient Architectures for Edge AI-Enabled Applications

GELEX: Generative AI-Hybrid System for Example-Based Learning

Accelerator-driven Data Arrangement to Minimize Transformers Run-time on Multi-core Architectures

Data-Driven Control Synthesis using Koopman Operator: A Robust Approach

Imaging sensor device using an array of single-photon avalanche diode photodetectors

Robust machine learning for neuroscientific inference

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Spectral Estimators for High-Dimensional Matrix Inference

Exploring brain-inspired multi-core heterogeneous hardware templates for low-power biomedical embedded systems