Andrea Bartolini
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View article: Co-designing a Programmable RISC-V Accelerator for MPC-based Energy and Thermal Management of Many-Core HPC Processors
Co-designing a Programmable RISC-V Accelerator for MPC-based Energy and Thermal Management of Many-Core HPC Processors Open
Managing energy and thermal profiles is critical for many-core HPC processors with hundreds of application-class processing elements (PEs). Advanced model predictive control (MPC) delivers state-of-the-art performance but requires solving …
View article: KubeIntellect: A Modular LLM-Orchestrated Agent Framework for End-to-End Kubernetes Management
KubeIntellect: A Modular LLM-Orchestrated Agent Framework for End-to-End Kubernetes Management Open
Kubernetes has become the foundation of modern cloud-native infrastructure, yet its management remains complex and fragmented. Administrators must navigate a vast API surface, manage heterogeneous workloads, and coordinate tasks across dis…
View article: An online algorithm for power consumption prediction of HPC workload
An online algorithm for power consumption prediction of HPC workload Open
As modern High-Performance Computing (HPC) systems push the boundaries of computational capabilities, their power consumption becomes a serious threat to environmental and energy sustainability. In such a context, accurate prediction of th…
View article: F-DATA: A Fugaku Workload Dataset for Job-centric Predictive Modelling in HPC Systems
F-DATA: A Fugaku Workload Dataset for Job-centric Predictive Modelling in HPC Systems Open
In the last decades, High Performance Computing (HPC) systems have accelerated scientific discoveries and innovations across different domains, from epidemic studies to climate science. For sustainable development of HPC systems, it is fun…
View article: A Unified Ontology for Scalable Knowledge Graph-Driven Operational Data Analytics in High-Performance Computing Systems
A Unified Ontology for Scalable Knowledge Graph-Driven Operational Data Analytics in High-Performance Computing Systems Open
Modern high-performance computing (HPC) systems generate massive volumes of heterogeneous telemetry data from millions of sensors monitoring compute, memory, power, cooling, and storage subsystems. As HPC infrastructures scale to support i…
View article: Assessing Tenstorrent's RISC-V MatMul Acceleration Capabilities
Assessing Tenstorrent's RISC-V MatMul Acceleration Capabilities Open
The increasing demand for generative AI as Large Language Models (LLMs) services has driven the need for specialized hardware architectures that optimize computational efficiency and energy consumption. This paper evaluates the performance…
View article: SpikeStream: Accelerating Spiking Neural Network Inference on RISC-V Clusters with Sparse Computation Extensions
SpikeStream: Accelerating Spiking Neural Network Inference on RISC-V Clusters with Sparse Computation Extensions Open
Spiking Neural Network (SNN) inference has a clear potential for high energy efficiency as computation is triggered by events. However, the inherent sparsity of events poses challenges for conventional computing systems, driving the develo…
View article: SpikeStream: Accelerating Spiking Neural Network Inference on RISC-V Clusters with Sparse Computation Extensions
SpikeStream: Accelerating Spiking Neural Network Inference on RISC-V Clusters with Sparse Computation Extensions Open
Spiking Neural Network (SNN) inference has a clear potential for high energy efficiency as computation is triggered by events. However, the inherent sparsity of events poses challenges for conventional computing systems, driving the develo…
View article: Efficient Trace for RISC-V: Design, Evaluation, and Integration in CVA6
Efficient Trace for RISC-V: Design, Evaluation, and Integration in CVA6 Open
In this work, we present the design and evaluation of a Processor Tracing System compliant with the RISC-V Efficient Trace specification for Instruction Branch Tracing. We integrate our system into the host domain of a state-of-the-art edg…
View article: The REGALE Library: A DDS Interoperability Layer for the HPC PowerStack
The REGALE Library: A DDS Interoperability Layer for the HPC PowerStack Open
Large-scale computing clusters have been the basis of scientific progress for several decades and have now become a commodity fuelling the AI revolution. Dark Silicon, energy efficiency, power consumption, and hot spots are no longer loomi…
View article: Performance Characterization of Hardware/Software Communication Interfaces in End-to-End Power Management Solutions of High-Performance Computing Processors
Performance Characterization of Hardware/Software Communication Interfaces in End-to-End Power Management Solutions of High-Performance Computing Processors Open
Power management (PM) is cumbersome for today’s computing systems. Attainable performance is bounded by the architecture’s computing efficiency and capped in temperature, current, and power. PM is composed of multiple interacting layers. H…
View article: MCBound: An Online Framework to Characterize and Classify Memory/Compute-bound HPC Jobs
MCBound: An Online Framework to Characterize and Classify Memory/Compute-bound HPC Jobs Open
Modern High-Performance Computing (HPC) systems play a fundamental role in driving scientific research, as they execute computationally intensive jobs originating from diverse domains. However, HPC jobs are characterized by conflicting com…
View article: ControlPULPlet: A Flexible Real-time Multi-core RISC-V Controller for 2.5D Systems-in-package
ControlPULPlet: A Flexible Real-time Multi-core RISC-V Controller for 2.5D Systems-in-package Open
The growing complexity of real-time control algorithms with increasing performance demands, along with the shift to 2.5D technology, drive the need for scalable controllers to manage chiplets' coupled operation in 2.5D systems-in-package. …
View article: Energy efficient and low-latency spiking neural networks on embedded microcontrollers through spiking activity tuning
Energy efficient and low-latency spiking neural networks on embedded microcontrollers through spiking activity tuning Open
In this work, we target the efficient implementation of spiking neural networks (SNNs) for low-power and low-latency applications. In particular, we propose a methodology for tuning SNN spiking activity with the objective of reducing compu…
View article: LIDAROC: Realistic LiDAR Cover Contamination Dataset for Enhancing Autonomous Vehicle Perception Reliability
LIDAROC: Realistic LiDAR Cover Contamination Dataset for Enhancing Autonomous Vehicle Perception Reliability Open
LiDAR is the foundation of many autonomous vehicle perception systems, so it is essential to study and ensure the integrity and robustness of the data collected by LiDAR. To facilitate future research into robust and resilient LiDAR proces…
View article: Unleashing OpenTitan's Potential: a Silicon-Ready Embedded Secure Element for Root of Trust and Cryptographic Offloading
Unleashing OpenTitan's Potential: a Silicon-Ready Embedded Secure Element for Root of Trust and Cryptographic Offloading Open
The rapid advancement and exploration of open-hardware RISC-V platforms are driving significant changes in sectors like autonomous vehicles, smart-city infrastructure, and medical devices. OpenTitan stands out as a groundbreaking open-sour…
View article: Exploring Spiking Neural Networks for Deep Reinforcement Learning in Robotic Tasks: A Comparative Study
Exploring Spiking Neural Networks for Deep Reinforcement Learning in Robotic Tasks: A Comparative Study Open
Spiking Neural Networks (SNNs) stand as the third generation of Artificial Neural Networks (ANNs), mirroring the functionality of the mammalian brain more closely than their predecessors. Their computational units, spiking neurons, charact…
View article: Modeling and Controlling Many-Core HPC Processors: an Alternative to PID and Moving Average Algorithms
Modeling and Controlling Many-Core HPC Processors: an Alternative to PID and Moving Average Algorithms Open
The race towards performance increase and computing power has led to chips with heterogeneous and complex designs, integrating an ever-growing number of cores on the same monolithic chip or chiplet silicon die. Higher integration density, …
View article: ExaQuery: Proving Data Structure to Unstructured Telemetry Data in Large-Scale HPC
ExaQuery: Proving Data Structure to Unstructured Telemetry Data in Large-Scale HPC Open
High-performance computing (HPC) is the cornerstone of technological advancements in our digital age, but its management is becoming increasingly challenging, particularly as systems approach exascale. Operational data analytics (ODA) and …
View article: Exploring the Utility of Graph Methods in HPC Thermal Modeling
Exploring the Utility of Graph Methods in HPC Thermal Modeling Open
This work critically examines several approaches to temperature prediction for High-Performance Computing (HPC) systems, focusing on component-level and holistic models. In particular, we use publicly available data from the Tier-0 Marconi…
View article: AutoGrAN: Autonomous Vehicle LiDAR Contaminant Detection using Graph Attention Networks
AutoGrAN: Autonomous Vehicle LiDAR Contaminant Detection using Graph Attention Networks Open
Extreme conditions and the integrity of LiDAR sensors influence AI perception models in autonomous vehicles. Lens contamination caused by external particles can compromise LiDAR object detection performance. Automatic contaminant detection…
View article: Assessing the Performance of OpenTitan as Cryptographic Accelerator in Secure Open-Hardware System-on-Chips
Assessing the Performance of OpenTitan as Cryptographic Accelerator in Secure Open-Hardware System-on-Chips Open
RISC-V open-source systems are emerging in deployment scenarios where safety and security are critical. OpenTitan is an open-source silicon root-of-trust designed to be deployed in a wide range of systems, from high-end to deeply embedded …
View article: NARS: Neuromorphic Acceleration through Register-Streaming Extensions on RISC-V Cores
NARS: Neuromorphic Acceleration through Register-Streaming Extensions on RISC-V Cores Open
Spiking Neural Networks (SNNs) have emerged as a promising bio-inspired solution to address the need for low-latency, energy-efficient artificial intelligence systems. SNNs pose a challenge to traditional CPUs, GPUs and neural network acce…