Bit-Line Computing for CNN Accelerators Co-Design in Edge AI Inference

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Integer linear programming (ILP) is an elegant approach to solve linear optimization problems, naturally described using integer decision variables. Within the context of physics-inspired machine learning (ML) applied to chemistry, we demonstrate the relev ...

The miniaturization of integrated circuits (ICs) and their higher performance and energy efficiency, combined with new machine learning algorithms and applications, have paved the way to intelligent, interconnected edge devices. In the medical domain, they ...

Machine learning is increasingly integrated with decision making by powering automated systems and informing human decision makers in applications such as CV screening, portfolio allocation, loan risk assessment, and insurance approval. In this thesis, I e ...

ML-based edge devices may face memory and computational errors that affect applications' reliability and performance. These errors can be the result of particular working conditions (e.g., radiation areas in physical experiments or avionics) or could be th ...

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 ...

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 ...

Adversarial examples crafted in black-box scenarios are affected by unrealistic colors or spatial artifacts. To prevent these shortcomings, we propose a novel strategy that generates adversarial images with low detectability and high transferability. The p ...

Gain-cell embedded DRAM (GC-eDRAM) is a high-density logic-compatible alternative to conventional static random-access memory (SRAM) and embedded DRAM (eDRAM). However, GC-eDRAM suffers from a reduced data retention time (DRT) at deeply-scaled process node ...

Many challenges of modern graph processing stem from the fact that graphs have become massive and change frequently. Numerous techniques have been developed to address those issues, with graph cut sparsification offering to replace original graphs with th ...

Virtual Memory (VM) is a critical programming abstraction that is widely used in various modern computing platforms. With the rise of datacenter computing and birth of planet-scale online services, the semantic and capacity requirements from memory have ev ...

Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving differential equations by integrating physical laws into the learning process. This work leverages PINNs to simulate gravitational collapse, a critical phenomenon in astro ...

Information derived from experiences is incorporated into the brain as changes to ensembles of cells, termed engram cells, which allow memory storage and recall. The mechanism by which those changes hold specific information is unclear. Here, we test the h ...

Traumatic events generate some of the most enduring memories, yet little is known about how long-lasting fear memories can be attenuated. In this review, we collect the surprisingly sparse evidence on remote fear memory attenuation from both animal and hum ...

The recollection of sensory information and subjective experience related to a personal past event depends on our episodic memory (EM). At the neural level, EM retrieval is linked with the reinstatement of hippocampal activity thought to recollect the sens ...

Empirical evidence shows that memories that are frequently revisited are easy to recall, and that familiar items involve larger hippocampal representations than less familiar ones. In line with these observations, here we develop a modelling approach to pr ...

Over the course of a lifetime, the human brain acquires an astonishing amount of semantic knowledge and autobiographical memories, often with an imprinting strong enough to allow detailed information to be recalled many years after the initial learning exp ...

Bit-Line Computing for CNN Accelerators Co-Design in Edge AI Inference

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Integer Linear Programming for Unsupervised Training Set Selection in Molecular Machine Learning

Exploring brain-inspired multi-core heterogeneous hardware templates for low-power biomedical embedded systems

Machine Learning in Decision-Making Systems: Fairness, Robustness, and Data Bias

Hardware-Software co-design Methodologies for Edge AI Optimization

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SPINN: Advancing Cosmological Simulations of Fuzzy Dark Matter with Physics Informed Neural Networks

Engram cell connectivity as a mechanism for information encoding and memory function

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Bodily self-consciousness as a framework to link sensory information and self-related components of episodic memory: behavioral, neuroimaging, and clinical evidence

A dynamic attractor network model of memory formation, reinforcement and forgetting

A theory of memory consolidation and synaptic pruning in cortical circuits