331 citations · 476 across the 9 of their papers we have counts for
9 papers
IMAGINE: An 8-to-1b 22nm FD-SOI Compute-In-Memory CNN Accelerator With an End-to-End Analog Charge-Based 0.15-8POPS/W Macro Featuring Distribution-Aware Data Reshaping
Adrian Kneip, Martin Lefebvre, Pol Maistriaux +1
Charge-domain compute-in-memory (CIM) SRAMs have recently become an enticing compromise between computing efficiency and accuracy to process sub-8b convolutional neural networks (C…
MANTIS: A Mixed-Signal Near-Sensor Convolutional Imager SoC Using Charge-Domain 4b-Weighted 5-to-84-TOPS/W MAC Operations for Feature Extraction and Region-of-Interest Detection
Martin Lefebvre, David Bol
Recent advances in artificial intelligence have prompted the search for enhanced algorithms and hardware to support the deployment of machine learning at the edge. More specificall…
A nA-Range Area-Efficient Sub-100-ppm/°C Peaking Current Reference Using Forward Body Biasing in 0.11-m Bulk and 22-nm FD-SOI
Martin Lefebvre, David Bol
In recent years, the development of the Internet of Things (IoT) has prompted the search for nA-range current references that are simultaneously constrained to a small area and rob…
A 2.5-nA Area-Efficient Temperature-Independent 176-/82-ppm/°C CMOS-Only Current Reference in 0.11-m Bulk and 22-nm FD-SOI
Martin Lefebvre, David Bol
Internet-of-Things (IoT) applications require nW-power current references that are robust to process, voltage and temperature (PVT) variations, to maintain the performance of IoT s…
A 1.1- / 0.9-nA Temperature-Independent 213- / 565-ppm/C Self-Biased CMOS-Only Current Reference in 65-nm Bulk and 22-nm FDSOI
Martin Lefebvre, Denis Flandre, David Bol
In many applications, the ability of current references to cope with process, voltage, and temperature (PVT) variations is critical to maintaining system-level performance. However…
A Family of Current References Based on 2T Voltage References: Demonstration in 0.18-m with 0.1-nA PTAT and 1.1-A CWT 38-ppm/C Designs
Martin Lefebvre, David Bol
The robustness of current and voltage references to process, voltage and temperature (PVT) variations is paramount to the operation of integrated circuits in real-world conditions.…
Energy reconstruction in a liquid argon calorimeter cell using convolutional neural networks
L. Polson, L. Kurchaninov, M. Lefebvre
The liquid argon ionization current in a sampling calorimeter cell can be analyzed to determine the energy of detected particles. In practice, experimental artifacts such as pileup…
Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks
Charlotte Frenkel, Martin Lefebvre, David Bol
While the backpropagation of error algorithm enables deep neural network training, it implies (i) bidirectional synaptic weight transport and (ii) update locking until the forward…
A 0.086-mm 12.7-pJ/SOP 64k-Synapse 256-Neuron Online-Learning Digital Spiking Neuromorphic Processor in 28nm CMOS
Charlotte Frenkel, Martin Lefebvre, Jean-Didier Legat +1
Shifting computing architectures from von Neumann to event-based spiking neural networks (SNNs) uncovers new opportunities for low-power processing of sensory data in applications…