CoreNEURON : An Optimized Compute Engine for the NEURON Simulator

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Neural organoids and spheroids are three-dimensional (3D) in vitro brain models that mimic key aspects of human brain cellular composition, organization, and function. These models have become essential for studying brain development and neurological disor ...

Function-as-a-Service (FaaS) has emerged as a popular cloud paradigm that simplifies software development and deployment by providing scalable and event-driven function execution without the burden of managing servers. FaaS was originally created with a fu ...

“You never forget how to ride a bike”, – but how is that possible? The brain is able to learn complex skills, stop the practice for years, learn other skills in between, and still retrieve the original knowledge when necessary. The mechanisms of this capab ...

Virtual Memory (VM) is a critical programming abstraction that is widely used in various modern computing platforms. With the rise of datacenter computing and birth of planet-scale online services, the semantic and capacity requirements from memory have ev ...

The demise of Moore's Law and Dennard scaling has resulted in diminishing performance gains for general-purpose processors, and so has prompted a surge in academic and commercial interest for hardware accelerators. Specialized hardware has already redefine ...

Machine learning and data processing algorithms have been thriving in finding ways of processing and classifying information by exploiting the hidden trends of large datasets. Although these emerging computational methods have become successful in today's ...

State-of-the-art Artificial Intelligence (AI) algorithms, such as graph neural networks and recommendation systems, require floating-point computation of very large matrix multiplications over sparse data. Their execution in resource-constrained scenarios, ...

Modern hardware is increasingly complex, requiring increasing effort to understand in order to carefully engineer systems for optimal performance and effective utilization. Moreover, established design principles and assumptions are not portable to modern ...

Deep learning has achieved remarkable success in various challenging tasks such as generating images from natural language or engaging in lengthy conversations with humans. The success in practice stems from the ability to successfully train massive neural ...

Embedded systems confront two opposite goals: low-power operation and high performance. The current trend to reach these goals is toward heterogeneous platforms, including multi-core architectures with heterogeneous cores and hardware accelerators. The lat ...

Cortical dynamics underlie many cognitive processes and emerge from complex multi-scale interactions, which are challenging to study in vivo. Large-scale, biophysically detailed models offer a tool which can complement laboratory approaches. We present a m ...

To build biophysically detailed models of brain cells, circuits, and regions, a data-driven approach is increasingly being adopted. This helps to obtain a simulated activity that reproduces the experimentally recorded neural dynamics as faithfully as possi ...

Sylvester matrix equations are ubiquitous in scientific computing. However, few solution techniques exist for their generalized multiterm version, as they now arise in an increasingly large number of applications. In this work, we consider algebraic parame ...

This paper presents a novel simulation approach for generating synthetic households, addressing several literature gaps from the methodological viewpoint. The generation of hierarchical datasets such as complete households is challenging since it must guar ...

Compute memories are memory arrays augmented with dedicated logic to support arithmetic. They support the efficient execution of data-centric computing patterns, such as those characterizing Artificial Intelligence (AI) algorithms. These architectures can ...

Agent-based simulations have been widely applied in many disciplines, by scientists and engineers alike. Scientists use agent-based simulations to tackle global problems, including alleviating poverty, reducing violence, and predicting the impact of pandem ...

Driven by the demand for real-time processing and the need to minimize latency in AI algorithms, edge computing has experienced remarkable progress. Decision-making AI applications stand out for their heavy reliance on data-centric operations, predominantl ...

Measuring bathymetry has always been a major scientific and technological challenge. In this work, we used a deep learning technique for inferring bathymetry from the depth-averaged velocity field. The training of the neural network is based on 5742 labora ...

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 ...

The manuscript describes the development of a spatial Agent-based Simulation to model the effect of public policies on private houseowner’s decisions concerning their heating system. The methodology utilized comprises of an empirical survey to determine th ...

CoreNEURON : An Optimized Compute Engine for the NEURON Simulator

Chattez avec Graph Search

Soft microelectrode arrays for 3D in vitro brain models

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Context selectivity with dynamic availability enables lifelong continual learning

Rebooting Virtual Memory with Midgard

Building Chips Faster: Hardware-Compiler Co-Design for Accelerated RTL Simulation

2D Nanosystems: Applications of 2D Semiconductors for In-Memory Computing

A 16-bit Floating-Point Near-SRAM Architecture for Low-power Sparse Matrix-Vector Multiplication

Chaosity: Understanding Contemporary NUMA-architectures

A Theory of Finite-Width Neural Networks: Generalization, Scaling Laws, and the Loss Landscape

Acceleration of Control Intensive Applications on Coarse-Grained Reconfigurable Arrays for Embedded Systems

Modeling and simulation of neocortical micro- and mesocircuitry. Part II: Physiology and experimentation

Online interoperable resources for building hippocampal neuron models via the Hippocampus Hub

Preconditioning techniques for generalized Sylvester matrix equations

Divide-and-conquer one-step simulator for the generation of synthetic households

Overflow-free compute memories for edge AI acceleration

Generalizing Bulk-Synchronous Parallel Processing for Data Science: From Data to Threads and Agent-Based Simulations

EdgeAI-Aware Design of In-Memory Computing Architectures

Bed Topography Inference from Velocity Field Using Deep Learning

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

Spatial Agent-Based Modelling and Simulation to Evaluate on Public Policies for Energy Transition