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Alexandre Sébastien Julien Levisse

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Related publications (37)

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A Fast, Reliable and Wide-voltage-range In-memory Computing Architecture

David Atienza Alonso, Marina Zapater Sancho, Alexandre Sébastien Julien Levisse, William Andrew Simon

In-Memory Computing (IMC) solutions, and particularly bitline computing in SRAM, appear promising as they mitigate one of the most energy consuming aspects in computation: data movement. In this work we propose a fast (2.4Ghz for bitwise operations and 2.4 ...

Association for Computing Machinery2019

Resistive Switching Memory Architecture Based on Polarity Controllable Selectors

Alexandre Sébastien Julien Levisse, Pierre-Emmanuel Julien Marc Gaillardon

With the continuous scaling of CMOS technology, integrating an embedded high-density non-volatile memory appears to be more and more costly and technologically challenging. Beyond floating-gate memory technologies, bipolar resistive random access memories ...

2018

A GRANDAB Decoder with 8.48 Gbps Worst-Case Throughput in 65nm CMOS

Victor Charles Jacques Herrmann, Andreas Peter Burg, Alexandre Sébastien Julien Levisse, Yifei Shen, Yuqing Ren, Andreas Toftegaard Kristensen, Thomas Christoph Müller, Ludovic Damien Blanc

We present an error correction code (ECC) decoder based on the Guessing Random Additive Noise Decoding (GRAND) with ABandonment (GRANDAB) algorithm for applications that require constant throughput and fixed decoding latency in the Gbps range. This high co ...

IEEE Computer Society2024

RRAMSpec: A Design Space Exploration Framework for High Density Resistive RAM

Alexandre Sébastien Julien Levisse

Resistive RAM (RRAM) is a promising emerging Non-Volatile Memory candidate due to its scalability and CMOS compatibility, which enables the fabrication of high density RRAM crossbar arrays in Back-End-Of-Line CMOS processes. Fast and accurate architectural ...

2019

Exact Neural Networks from Inexact Multipliers via Fibonacci Weight Encoding

David Atienza Alonso, Giovanni Ansaloni, Marina Zapater Sancho, Alexandre Sébastien Julien Levisse, William Andrew Simon

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 ...

2021

A Flexible In-Memory Computing Architecture for Heterogeneously Quantized CNNs

David Atienza Alonso, Giovanni Ansaloni, Alexandre Sébastien Julien Levisse, Flavio Ponzina, Marco Antonio Rios

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 ...

2021

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

David Atienza Alonso, Giovanni Ansaloni, Marina Zapater Sancho, Alexandre Sébastien Julien Levisse, Halima Najibi

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 ...

2022

Enabling Optimal Power Generation of Flow Cell Arrays in 3D MPSoCs with On-Chip Switched Capacitor Converters

David Atienza Alonso, Marina Zapater Sancho, Alexandre Sébastien Julien Levisse, Halima Najibi

Flow cell arrays (FCAs) provide efficient on-chip liquid cooling and electrochemical power generation capabilities in three-dimensional multi-processor systems-on-chip (3DMPSoCs). When connected to power delivery networks (PDNs) of chips, the current flowi ...

IEEE2020

RRAMSpec: A Design Space Exploration Framework for High Density Resistive RAM

Alexandre Sébastien Julien Levisse

2019

Analysis of Functional Errors Produced by Long-Term Workload-Dependent BTI Degradation in Ultra-Low Power Processors

David Atienza Alonso, Alexandre Sébastien Julien Levisse, Miguel Peon Quiros, Ruben Braojos Lopez, Loris Gérard Duch

Aging effects in digital circuits change the switching characteristics of their transistors, resulting in timing violations that can lead to functional errors at the system level. In particular, BTI is a degradation effect that changes the threshold voltag ...

2020

Functionality Enhanced Memories for Edge-AI Embedded Systems

David Atienza Alonso, Alexandre Sébastien Julien Levisse, Pierre-Emmanuel Julien Marc Gaillardon, Marco Antonio Rios, William Andrew Simon

With the surge in complexity of edge workloads, it appeared in the scientific community that such workloads cannot be anymore overflown to the cloud due to the huge edge device to server communication energy cost and the high energy consumption induced in ...

IEEE2019

Memory chip or memory array for wide-voltage range in-memory computing using bitline technology

David Atienza Alonso, Marina Zapater Sancho, Alexandre Sébastien Julien Levisse, Marco Antonio Rios, William Andrew Simon

A random access memory having a memory array having a plurality of local memory groups, each local memory group including a plurality of bitcells arranged in a bitcell column, a pair of local bitlines operatively connected to the plurality of bitcells, a p ...

2021

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

David Atienza Alonso, Giovanni Ansaloni, Alexandre Sébastien Julien Levisse

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 ...

2021

Associativity-agnostic in-cache computing memory architecture optimized for multiplication

David Atienza Alonso, Marina Zapater Sancho, Alexandre Sébastien Julien Levisse, Marco Antonio Rios

A random access memory array including a plurality of local memory group ways, each local memory group way including, a plurality of local memory groups, each local memory group including, a memory column including a plurality of memory cells, a pair of lo ...

2021

DBFS: Dynamic Bitwidth-Frequency Scaling for Efficient Software-defined SIMD

Giovanni Ansaloni, Alexandre Sébastien Julien Levisse, Flavio Ponzina, Pengbo Yu

Machine learning algorithms such as Convolutional Neural Networks (CNNs) are characterized by high robustness towards quantization, supporting small-bitwidth fixed-point arithmetic at inference time with little to no degradation in accuracy. In turn, small ...

IEEE2024

Write Termination circuits for RRAM : A Holistic Approach From Technology to Application Considerations

David Atienza Alonso, Alexandre Sébastien Julien Levisse, Marco Antonio Rios

While Resistive Random Access Memories (RRAM) are perceived nowadays as a promising solution for the future of computing, these technologies suffer from intrinsic variability regarding programming voltage, switching speed and achieved resistance values. Wr ...

IEEE2020

Blade: An in-Cache Computing Architecture for Edge Devices

David Atienza Alonso, Marina Zapater Sancho, Alexandre Sébastien Julien Levisse, Marco Antonio Rios, William Andrew Simon, Yasir Mahmood Qureshi

Area and power constrained edge devices are increasingly utilized to perform compute intensive workloads, necessitating increasingly area and power efficient accelerators. In this context, in-SRAM computing performs hundreds of parallel operations on spati ...

2020