TamaRISC-CS: An Ultra-Low-Power Application-Specific Processor for Compressed Sensing

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Implanted medical devices (IMDs) have been widely developed to support the monitoring and recording of biological data inside the body or brain. Wirelessly powered IMDs, a subset of implantable electronics, have been proposed to eliminate the limitations r ...

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 ...

In inverse problems, the task is to reconstruct an unknown signal from its possibly noise-corrupted measurements. Penalized-likelihood-based estimation and Bayesian estimation are two powerful statistical paradigms for the resolution of such problems. They ...

A modular multiple frequency coils inductive link system to wirelessly provide power for at least a medical implant at an output of a receiving coil, whereby the receiving coil is configured to be implanted in an organism. The modular multiple frequency co ...

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 ...

Today's continued increase in demand for processing power, despite the slowdown of Moore's law, has led to an increase in processor count, which has resulted in energy consumption and distribution problems. To address this, there is a growing trend toward ...

This paper presents a new power-efficient and high-speed voltage level shifter. In the proposed structure, the existing contentions at the internal nodes are reduced using auxiliary transistors and feedback networks, leading to a significant reduction in t ...

The goal of this thesis is to study continuous-domain inverse problems for the reconstruction of sparse signals and to develop efficient algorithms to solve such problems computationally. The task is to recover a signal of interest as a continuous function ...

Field-programmable gate arrays (FPGAs) combine hardware reconfigurability with a high degree of parallelism. Consequently, FPGAs offer performance gains and power savings for many applications. A recent trend has been to leverage the hardware versatility o ...

Flow Cell Arrays (FCA) technology employs microchannels filled with an electrolytic fluid to concurrently provide cooling and power generation to integrated circuits (ICs). This solution is particularly appealing for Three-Dimensional Multi-Processor Syste ...

Computing servers play a key role in the development and process of emerging compute-intensive applications in recent years. However, they need to operate efficiently from an energy perspective viewpoint, while maximizing the performance and lifetime of th ...

FPGAs are proving useful and attractive for many applications, thanks to their hardware reconfigurability, low power, and high-degree of parallelism. As a result, modern embedded systems are often based on systems-on-chip (SoCs), where CPUs and FPGAs share ...

Flow cell arrays (FCAs) concurrently provide efficient on-chip liquid cooling and electrochemical power generation. This technology is especially promising for three-dimensional multi-processor systems-on-chip (3D MPSoCs) realized in deeply scaled technolo ...

Utilization of edge devices has exploded in the last decade, with such use cases as wearable devices, autonomous driving, and smart homes. As their ubiquity grows, so do expectations of their capabilities. Simultaneously, their formfactor and use cases lim ...

Fast and accurate transmission line outage detection can help the central control unit to respond rapidly to better maintain the security and reliability of power systems. It is especially critical in the situation of multiple line outages which is more li ...

Deeply-scaled three-dimensional (3D) Multi-Processor Systems-on-Chip (MPSoCs) enable high performance and massive communication bandwidth for next-generation computing. However, as process nodes shrink, temperature-dependent leakage dramatically increases, ...

Advances of deep learning for Artificial Neural Networks (ANNs) have led to significant improvements in the performance of digital signal processing systems implemented on digital chips. Although recent progress in low-power chips is remarkable, neuromorph ...

Deep neural networks often have millions of parameters. This can hinder their deployment to low-end devices, not only due to high memory requirements but also because of increased latency at inference. We propose a novel model compression method that gener ...

In the recent Internet-of-Things (IoT) era where biomedical applications require continuous monitoring of relevant data, edge computing keeps gaining more and more importance. These new architectures for edge computing include multi-core and parallel compu ...

One of the greatest challenges in the design of modern wearable devices is energy efficiency. While data processing and communication have received a lot of attention from the industry and academia, leading to highly efficient microcontrollers and transmis ...

TamaRISC-CS: An Ultra-Low-Power Application-Specific Processor for Compressed Sensing

Chattez avec Graph Search

Integrated Wireless Power, Data Communication, and Thermal Sensing System for Autonomous Multisite Brain Implants

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

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

A modular multiple frequency inductive link to wirelessly power multiple miniaturized implants

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

Compilation and Design Space Exploration of Dataflow Programs for Heterogeneous CPU-GPU Platforms

A Delay and Power Efficient Voltage Level Shifter with Low Leakage Power

Variational Methods For Continuous-Domain Inverse Problems: the Quest for the Sparsest Solution

DFAulted: Analyzing and Exploiting CPU Software Faults Caused by FPGA-Driven Undervolting Attacks

Thermal and Power-Aware Run-Time Performance Management of 3D MPSoCs with Integrated Flow Cell Arrays

Reinforcement Learning-Based Joint Reliability and Performance Optimization for Hybrid-Cache Computing Servers

FPGA-to-CPU Undervolting Attacks

Thermal and Voltage-Aware Performance Management of 3D MPSoCs with Flow Cell Arrays and Integrated SC Converters

In-Memory Hardware and Architectural Extensions for Workloads Acceleration

Multiple Line Outage Detection in Power Systems by Sparse Recovery Using Transient Data

Towards Deeply Scaled 3D MPSoCs with Integrated Flow Cell Array Technology

A Bin Encoding Training Of A Spiking Neural Network Based Voice Activity Detection

Dynamic Model Pruning with Feedback

Real-Time Personalized Atrial Fibrillation Prediction on Multi-Core Wearable Sensors

An Event-Based System for Low-Power ECG QRS Complex Detection