Low-Cost Side-Channel Secure Standard 6T-SRAM-Based Memory With a 1% Area and Less Than 5% Latency and Power Overheads

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The increasing demand for computing power and the emergence of heterogeneous computing architectures have driven the exploration of innovative techniques to address current limitations in both the compute and memory subsystems. One such solution is the use ...

Driven by the demand for real-time processing and the need to minimize latency in AI algorithms, edge computing has experienced remarkable progress. Decision-making AI applications stand out for their heavy reliance on data-centric operations, predominantl ...

Comprehensive memory safety validation identifies the memory objects whose accesses provably comply with all classes of memory safety, protecting them from memory errors elsewhere at low overhead. We assess the breadth and depth of comprehensive memory saf ...

Embedded memories occupy an increasingly dominant part of the area and power budgets of modern systems-on-chips (SoCs). Multi-ported embedded memories, commonly used by media SoCs and graphical processing units, occupy even more area and consume higher pow ...

A frequency scaling governor is critical for the performance management of cloud servers, as it enhances energy efficiency and helps to control operational temperatures, thereby ensuring system reliability. However, our in-depth analysis of the application ...

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 ...

Machine learning and data processing algorithms have been thriving in finding ways of processing and classifying information by exploiting the hidden trends of large datasets. Although these emerging computational methods have become successful in today's ...

How the 'what', 'where', and 'when' of past experiences are stored in episodic memories and retrieved for suitable decisions remains unclear. In an effort to address these questions, the authors present computational models of neural networks that behave l ...

Accessing input data is a critical operation in data analytics: i) slow data access significantly degrades performance, and ii) storing everything in the fastest medium, i.e., memory, incurs high operational and hardware costs. Further, while GPUs offer in ...

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 ...

Memory devices have returned to the spotlight due to increasing interest in using in-memory computing architectures to make data-driven algorithms more energy-efficient. One of the main advantages of this architecture is the efficient performance of vector ...

Modern programs are monolithic, combining code of varied provenance without isolation, all the while running on network-connected devices. A vulnerability in any component may compromise code and data of all other components. Compartmentalization separates ...

Analytical engines rely on in-memory data caching to avoid storage accesses and provide timely responses by keeping the most frequently accessed data in memory. Purely frequency- and time-based caching decisions, however, are a proxy of the expected query ...

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 ...

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 ...

Modern hardware is increasingly complex, requiring increasing effort to understand in order to carefully engineer systems for optimal performance and effective utilization. Moreover, established design principles and assumptions are not portable to modern ...

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 ...

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 ...

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 ...

Understanding user’s perception of service variability is essential to discern their overall perception of any type of (transport) service. We study the perception of waiting time variability for ride-hailing services. We carried out a stated preference su ...

Low-Cost Side-Channel Secure Standard 6T-SRAM-Based Memory With a 1% Area and Less Than 5% Latency and Power Overheads

Chattez avec Graph Search

Intermediate Address Space: virtual memory optimization of heterogeneous architectures for cache-resident workloads

EdgeAI-Aware Design of In-Memory Computing Architectures

Comprehensive Memory Safety Validation: An Alternative Approach to Memory Safety

Multi-Ported GC-eDRAM Bitcell with Dynamic Port Configuration and Refresh Mechanism

Is the powersave governor really saving power?

Rebooting Virtual Memory with Midgard

2D Nanosystems: Applications of 2D Semiconductors for In-Memory Computing

Computational models of episodic-like memory in food-caching birds

HetCache: Synergising NVMe Storage and GPU acceleration for Memory-Efficient Analytics

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

How to Achieve Large-Area Ultra-Fast Operation of MoS 2 Monolayer Flash Memories?

SecureCells: A Secure Compartmentalized Architecture

HPCache: memory-efficient OLAP through proportional caching revisited

Face your fears: attenuating remote fear memories by reconsolidation-updating

A theory of memory consolidation and synaptic pruning in cortical circuits

Chaosity: Understanding Contemporary NUMA-architectures

Hardware-Software co-design Methodologies for Edge AI Optimization

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

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

An instance-based learning approach for evaluating the perception of ride-hailing waiting time variability