Related papers: Improving FPGA resilience through Partial Dynamic …
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…
Reconfigurable intelligent surfaces (RISs) are anticipated to transform wireless communication in a way that is both economical and energy efficient. Revealing the practical power consumption characteristics of RISs can provide an essential…
In this paper, a remote FPGA-configuration method based on JTAG extension over optical fibers is presented. The method takes advantage of commercial components and ready-to-use software such as iMPACT and does not require any hardware or…
The ever growing demands of embedded systems to satisfy high computing performance and cost efficiency lead to the trend of using commercial off-the-shelf hardware. However, due to their highly integrated design they are becoming…
RowHammer attacks are a growing security and reliability concern for DRAMs and computer systems as they can induce many bit errors that overwhelm error detection and correction capabilities. System-level solutions are needed as process…
Non-Volatile Memories (NVMs) such as Resistive RAM (RRAM) are used in neuromorphic systems to implement high-density and low-power analog synaptic weights. Unfortunately, an RRAM cell can switch its state after reading its content a certain…
Today's systems have diverse needs that are difficult to address using one-size-fits-all commodity DRAM. Unfortunately, although system designers can theoretically adapt commodity DRAM chips to meet their particular design goals (e.g., by…
Nonvolatile phase change random access memory (PCRAM) is regarded as one of promising candidates for emerging mass storage in the era of Big Data. However, relatively high programming energy hurdles the further reduction of power…
Hardware specialization is commonly viewed as a way to scale performance in the dark silicon era with modern-day SoCs featuring multiple tens of dedicated accelerators. By only powering on hardware circuitry when needed, accelerators…
Data movement is the dominating factor affecting performance and energy in modern computing systems. Consequently, many algorithms have been developed to minimize the number of I/O operations for common computing patterns. Matrix…
Large-scale integration of emerging nanoscale non-volatile memory devices, e.g. resistive random-access memory (RRAM), can enable a new generation of neuromorphic computers that can solve a wide range of machine learning problems. Such…
Neural networks achieve state-of-the-art performance in image classification, speech recognition, scientific analysis and many more application areas. Due to the high computational complexity and memory footprint of neural networks, various…
Electronic circuits and systems used in mission and safety-critical applications usually employ redundancy in the design to overcome arbitrary fault(s) or failure(s) and guarantee the correct operation. In this context, the distributed…
Heterogeneous embedded systems, with diverse computing elements and accelerators such as FPGAs, offer a promising platform for fast and flexible ML inference, which is crucial for services such as autonomous driving and augmented reality,…
Quantum Random Access Memory (QRAM) holds the promise of enabling several large scale applications of quantum computers. However, designing fault tolerant QRAMs for large scale applications is still an open problem due to the poor error and…
Designing and optimizing FPGA overlays is a complex and time-consuming process, often requiring multiple trial-and-error iterations to determine a suitable configuration. This paper presents an AI-driven approach to optimizing FPGA overlay…
Developing robotic manipulation policies is iterative and hypothesis-driven: researchers test tactile sensing, gripper geometries, and sensor placements through real-world data collection and training. Yet even minor end-effector changes…
This paper investigates the novel one-sided communication methods based on remote memory access (RMA) operations in MPI for dynamic resizing of malleable applications, enabling data redistribution with minimal impact on application…
Emerging applications of control, estimation, and machine learning, ranging from target tracking to decentralized model fitting, pose resource constraints that limit which of the available sensors, actuators, or data can be simultaneously…
Dynamic Random Access Memory (DRAM) is pervasive in computer systems. Cell vulnerabilities caused by unintended phenomena (forced retention failure, latency alteration, rowhammer and rowpress) lead to unintended bit flips in memory. These…