BLADE: A BitLine Accelerator for Devices on the Edge

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Object detection, a fundamental task in computer vision, is crucial for various intelligent edge computing applications. However, object detection algorithms are usually heavy in computation, hindering their deployments on resource-constrained edge devices ...

Distributed systems, which enable the efficient processing of large-scale data and complex tasks across multiple interconnected processes, offer significant advantages over single-machine systems. These include enhanced scalability, improved fault toleran ...

Driving continuous, low-power artificial intelligence (AI) in the Internet of Things (IoT) requires reliable energy harvesting and storage under indoor or low-light conditions, where batteries face constraints such as finite lifetimes and increased environ ...

Understanding urban perception enhances urban intelligence research, aiding sustainable development and smart city. Urban Visual-Spatial Intelligence (UVSI) integrates human and sensor perception. This Perspective explores UVSI's potential for sustainable ...

This thesis aimed to estimate the effect of demographic and biomechanical preoperative variables on total shoulder arthroplasty (TSA) outcomes. To achieve this, various biomechanical variables were calculated using a clinical database comprising normal and ...

The desire and ability to place AI-enabled applications on the edge has grown significantly in recent years. However, the compute-, area-, and power-constrained nature of edge devices are stressed by the needs of the AI-enabled applications, due to a gener ...

In this paper, we are presenting a new architecture to directly compute the ‘Percent-free PSA’ diagnostics index to enhance the differentiation of prostate cancer from benign prostatic disease. The proposed circuit uses silicon nanowires-based memristive b ...

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 ...

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 ...

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 ...

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 ...

Compute memories are memory arrays augmented with dedicated logic to support arithmetic. They support the efficient execution of data-centric computing patterns, such as those characterizing Artificial Intelligence (AI) algorithms. These architectures can ...

Computer systems rely heavily on abstraction to manage the exponential growth of complexity across hardware and software. Due to practical considerations of compatibility between components of these complex systems across generations, developers have favou ...

Electronic devices play an irreplaceable role in our lives. With the tightening time to market, exploding demand for computing power, and continuous desire for smaller, faster, less energy-consuming, and lower-cost chips, computer-aided design for electron ...

Artificial Intelligence (AI) techniques are considered the most advanced approaches for diagnosing faults in power transformers. Dissolved Gas Analysis (DGA) is the conventional approach widely adopted for diagnosing incipient faults in power transformers. ...

Artificial Neural Networks (ANN) are habitually trained via the back-propagation (BP) algorithm. This approach has been extremely successful: Current models like GPT-3 have O(10 11 ) parameters, are trained on O(10 11 ) words and produce awe-inspiring resu ...

Machine learning algorithms such as Convolutional Neural Networks (CNNs) are characterized by high robustness towards quantization, supporting small-bitwidth fixed-point arithmetic at inference time with little to no degradation in accuracy. In turn, small ...

In the past decades, a significant increase of the transistor density on a chip has led to exponential growth in computational power driven by Moore's law. To overcome the bottleneck of traditional von-Neumann architecture in computational efficiency, effo ...

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 ...

Multi-Scale computing systems aim at bringing the computing as close as possible to the data sources, to optimize both computation and networking. These systems are composed of at least three computing layers: the terminal layer, the edge layer, and the cl ...

BLADE: A BitLine Accelerator for Devices on the Edge

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Edge-Cloud Collaborated Object Detection via Bandwidth Adaptive Difficult-Case Discriminator

Optimal Byzantine Agreement with Little Cryptography

Unlocking high-performance photocapacitors for edge computing in low-light environments

Urban Visual-spatial Intelligence: Linking Human and Sensor Perception for Sustainable Urban Development

Multimodal Prediction Framework for Total Shoulder Arthroplasty Outcomes: Integrating Morphological, Musculoskeletal, Finite Element, and Bayesian Causal Models

Exploring High-Performance and Energy-Efficient Architectures for Edge AI-Enabled Applications

Enhancing Prostate Cancer Diagnosis with In-Memory Sensing and Edge Computing of Percent-free PSA Index

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

Compilation and Design Space Exploration of Dataflow Programs for Heterogeneous CPU-GPU Platforms

EdgeAI-Aware Design of In-Memory Computing Architectures

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

Overflow-free compute memories for edge AI acceleration

Secure Interface Design Leveraging Hardware/Software Support

Contemporary Logic Synthesis: with an Application to AQFP Circuit Optimization

Power Transformer Fault Diagnosis Using Neural Network Optimization Techniques

Artificial Neural Network Training on an Optical Processor via Direct Feedback Alignment

DBFS: Dynamic Bitwidth-Frequency Scaling for Efficient Software-defined SIMD

Towards Scalable Electronics: Synthetic 2D Materials for Large-Area 2D Circuit Integration

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

Special Session: Challenges and Opportunities for Sustainable Multi-Scale Computing Systems