相关论文: A New Embedded Measurement Structure for eDRAM Cap…

Mass characterisation of emerging memory devices is an essential step in modelling their behaviour for integration within a standard design flow for existing integrated circuit designers. This work develops a novel characterisation platform…

In-memory computing is an emerging non-von Neumann computing paradigm where certain computational tasks are performed in memory by exploiting the physical attributes of the memory devices. Memristive devices such as phase-change memory…

Crossbar arrays of resistive memories (RRAM) hold the promise of enabling In-Memory Computing (IMC), but essential challenges due to the impact of device imperfection and device endurance have yet to be overcome. In this work, we…

As conventional technology scaling approaches physical and power limitations, modern computing systems increasingly face performance bottlenecks arising from memory latency, energy consumption, scalability constraints, and data movement…

The importance of low power consumption is widely acknowledged due to the increasing use of portable devices, which require minimizing the consumption of energy. The energy in a computational system depends heavily on the software being…

Deeply embedded systems often have the tightest constraints on energy consumption, requiring that they consume tiny amounts of current and run on batteries for years. However, they typically execute code directly from flash, instead of the…

This paper presents a set of models dedicated to describe a flash storage subsystem structure, functions, performance and power consumption behaviors. These models cover a large range of today's NAND flash memory applications. They are…

Over the past two decades, the storage capacity and access bandwidth of main memory have improved tremendously, by 128x and 20x, respectively. These improvements are mainly due to the continuous technology scaling of DRAM (dynamic…

Modern multi-core systems have a large number of design parameters, most of which are discrete-valued, and this number is likely to keep increasing as chip complexity rises. Further, the accurate evaluation of a potential design choice is…

In recent years, due to a higher demand for portable devices, which provide restricted amounts of processing capacity and battery power, the need for energy and time efficient hard- and software solutions has increased. Preliminary…

Output capacitors are among the most critical components in power electronic converters, as their degradation directly affects system stability and reliability. A key indicator of capacitor health is the Equivalent Series Resistance (ESR),…

RRAM technology has experienced explosive growth in the last decade, with multiple device structures being developed for a wide range of applications. However, transitioning the technology from the lab into the marketplace requires the…

In-memory computing technology is used extensively in artificial intelligence devices due to lower power consumption and fast calculation of matrix-based functions. The development of such a device and its integration in a system takes a…

With the rapid growth of deep neural networks (DNNs), compute-in-memory (CIM) has emerged as a promising energy-efficient paradigm for accelerating multiply-and-accumulate (MAC) operations. Yet, current CIM architectures are largely limited…

Processing-using-DRAM has been proposed for a limited set of basic operations (i.e., logic operations, addition). However, in order to enable the full adoption of processing-using-DRAM, it is necessary to provide support for more complex…

This paper presents an in-memory computing (IMC) architecture developed on an 8x8 array of 8T SRAM cells. This architecture enables both multi-bit parallel Multiply-Accumulate (MAC) operations and standard memory processing through…

The increasing growth of applications' memory capacity and performance demands has led the CPU vendors to deploy heterogeneous memory systems either within a single system or via disaggregation. For instance, systems like Intel's Knights…

Digital In-memory computing improves energy efficiency and throughput of a data-intensive process, which incur memory thrashing and, resulting multiple same memory accesses in a von Neumann architecture. Digital in-memory computing involves…

Embedding vector operations are a key component of modern deep neural network workloads. Unlike matrix operations with deterministic access patterns, embedding vector operations exhibit input data-dependent and non-deterministic memory…

Memory corruption vulnerabilities have been around for decades and rank among the most prevalent vulnerabilities in embedded systems. Yet this constrained environment poses unique design and implementation challenges that significantly…