Publications by Giovanni Ansaloni

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Edge devices must support computationally demanding algorithms, such as neural networks, within tight area/energy budgets. While approximate computing may alleviate these constraints, limiting induced errors remains an open challenge. In this paper, we pro ...

Full-System (FS) simulation is essential for performance evaluation of complete systems that execute complex applications on a complete software stack consisting of an operating system and user applications. Nevertheless, they require careful fine-tuning a ...

Inferences using Convolutional Neural Networks (CNNs) are resource and energy intensive. Therefore, their execution on highly constrained edge devices demands the careful co-optimization of algorithms and hardware. Addressing this challenge, in this paper ...

At the intersection between traditional CPU architectures and more specialized options such as FPGAs or ASICs lies the family of reconfigurable hardware architectures, termed Coarse-Grained Reconfigurable Arrays (CGRAs). CGRAs are composed of a 2-dimension ...

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 ...

In the past decade, computing systems have significantly increased in complexity and power consumption. Nowadays, heterogeneous multi-processor systems-on-chip (MPSoCs) integrate many computing cores. Heterogeneous MPSoCs often comprise general-purpose pro ...

There is great interest in "end-to-end" analysis that captures how innovation at the materials, device, and/or architectural levels will impact figures of merit at the application-level. However, there are numerous combinations of devices and architectures ...

This paper introduces a novel computing architecture devoted to the ultra-low power analysis of multiple bio-signals. Its structure comprises several processors interfaced with a shared acceleration resource, implemented as a Coarse Grained Reconfigurable ...

In the Internet-of-Things (IoT) era, there is an increasing trend to enable intelligent behavior in edge computing sensors. Thus, a new generation of smart wearable devices for health monitoring is being developed, able to perform complex Digital Signal Pr ...

Smart edge sensors for bio-signal monitoring must support complex signal processing routines within an extremely small energy envelope. Coarse-Grained Reconfigurable Arrays (CGRAs) are good candidates for tackling these conflicting objectives because, than ...

In order to develop sustainable and more powerful information technology (IT) infrastructures, the challenges posed by the "memory wall" are critical for the design of high-performance and high-efficiency many-core computing systems. In this context, recen ...

Near-DRAM computing strategies advocate for providing computational capabilities close to where data is stored. Although this paradigm can effectively address the memoryto-processor communication bottleneck, it also presents new challenges: The strict reso ...

Embedded biosignal analysis involves a considerable amount of parallel computations, which can be exploited by employing low-voltage and ultra-low-power (ULP) parallel computing architectures. By allowing data and instruction broadcasting, single instructi ...

This paper presents the set of simulation means used to develop the concept of N2C2 (neural network compute cube) based on a vertical transistor technology platform. On the basis of state-of-the-art junctionless nanowire transistors (JLNT), TCAD simulation ...

Smart Wireless Body Sensor Nodes (WBSNs) are a novel class of unobtrusive, battery-powered devices allowing the continuous monitoring and real-time interpretation of a subject’s bio-signals. One of its most relevant applications is the acquisition and anal ...

Wearable devices performing advanced bio-signal analysis algorithms will foster a revolution in healthcare provision of chronic cardiac diseases. In this context, energy efficiency is of paramount importance, as long-term operations must be performed withi ...

Edge-AI applications necessitate the joint application of hardware acceleration and software optimization to meet energy and area constraints. However, the co-design of these systems is hindered by the lack of integration options for novel hardware prototy ...

Wearable Health Companions allow the unobtrusive monitoring of patients affected by chronic conditions. In particular, by acquiring and interpreting bio-signals, they enable the detection of acute episodes in cardiac and neurological ailments. Nevertheless ...

Event detection and classification algorithms are resilient towards aggressive resource-aware optimisations. In this paper, we leverage this characteristic in the context of smart health monitoring systems. In more detail, we study the attainable benefits ...

In this work, we propose an open-hardware low-power coarse-grained reconfigurable array connected to a lightweight microcontroller and enclosed in an application mapping framework. The latter provides complete support to configure kernels in the reconfigur ...

Giovanni Ansaloni

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Exact Neural Networks from Inexact Multipliers via Fibonacci Weight Encoding

Towards Accurate RISC-V Full System Simulation via Component-level Calibration

A Flexible In-Memory Computing Architecture for Heterogeneously Quantized CNNs

A flexible framework for early power and timing comparison of time-multiplexed CGRA kernel executions

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

Cross-layer Exploration of 2.5D Energy-Efficient Heterogeneous Chiplets Integration: From System Simulation to Open Hardware

Cross Layer Design for the Predictive Assessment of Technology-Enabled Architectures

A Multi-Core Reconfigurable Architecture for Ultra-Low Power Bio-Signal Analysis

Heterogeneous and Inexact: Maximizing Power Efficiency of Edge Computing Sensors for Health Monitoring Applications

i-DPs CGRA: An Interleaved-Datapaths Reconfigurable Accelerator for Embedded Bio-signal Processing

Full System Exploration of On-Chip Wireless Communication on Many-Core Architectures

Bank on Compute-near-Memory: Design Space Exploration of Processing-near-Bank Architectures

Synchronizing Code Execution on Ultra-Low-Power Embedded Multi-Channel Signal Analysis Platforms

Modelling of vertical and ferroelectric junctionless technology for efficient 3D neural network compute cube dedicated to embedded artificial intelligence

A Methodology for Embedded Classification of Heartbeats Using Random Projections

Heterogeneous Error-Resilient Scheme for Spectral Analysis in Ultra-Low Power Wearable Electrocardiogram Devices

HEEPstor: an Open-Hardware Co-design Framework for Quantized Machine Learning at the Edge

INCLASS: Incremental Classification Strategy for Self-Aware Epileptic Seizure Detection

Tailoring SVM Inference for Resource-Efficient ECG-Based Epilepsy Monitors

An Open-Hardware Coarse-Grained Reconfigurable Array for Edge Computing