Publications

Magnetic skyrmions are nanoscale bubbles that emerge in specific materials due to unique magnetic interactions. These structures are not only stable but also require minimal energy to manipulate, positioning them as promising elements for next-generation electronic devices. Their inherent properties such as topological stability, low power requirements, and potential for miniaturization make them ideal for developing energy-efficient computing systems, including logic gates, arithmetic units, and in-memory computing architectures, as well as artificial neurons. This feature review explores the extensive range of applications for skyrmions, emphasizing their role in both fundamental computing operations and complex AI systems. It discusses the dynamics of skyrmions and their integration into basic and advanced AI architectures. The review aims to synthesize recent progress in the field of skyrmionics, highlighting their capability to revolutionize future computing technologies by improving energy efficiency and system scalability. Furthermore, the review explores the reciprocal relationship between AI and skyrmion technology. It examines how AI can enhance the understanding and optimization of skyrmion systems, thereby boosting their effectiveness in AI applications. Conversely, it also considers how skyrmion-based technologies facilitate advancements in AI, creating a bi-directional flow of benefits. This dual focus not only underscores the versatility of skyrmions in AI contexts but also highlights the symbiotic advancements achievable through this emerging technology integration leading to pathways in AI for skyrmion along with skyrmion for AI.

In order to follow the ever-growing computational complexity and data intensity of state-of-the-art AI models, new computing paradigms are being proposed. These paradigms aim at achieving high energy efficiency by mitigating the Von Neumann bottleneck that relates to the energy cost of moving data between the processing cores and the memory. Convolutional Neural Networks (CNNs) are susceptible to this bottleneck, given the massive data they have to manage. Systolic arrays (SAs) are promising architectures to mitigate data transmission cost, thanks to high data utilization of Processing Elements (PEs). These PEs continuously exchange and process data locally based on specific dataflows (such as weight stationary and row stationary), in turn reducing the number of memory accesses to the main memory. In SAs, convolutions are managed either as matrix multiplications or exploiting the raster-order scan of sliding windows. However, data redundancy is a primary concern affecting area, power, and energy. In this paper, we propose TrIM: a novel dataflow for SAs based on a Triangular Input Movement and compatible with CNN computing. TrIM maximizes the local input utilization, minimizes the weight data movement, and solves the data redundancy problem. Furthermore, TrIM does not incur the significant on-chip memory penalty introduced by the row stationary dataflow. When compared to state-of-the-art SA dataflows, the high data utilization offered by TrIM guarantees $\sim 10\times$ less memory access. Furthermore, considering that PEs continuously overlap multiplications and accumulations, TrIM achieves high throughput (up to 81.8% higher than row stationary), other than requiring a limited number of registers (up to $15.6\times$ fewer registers than row stationary).

In this demonstration, we present a hardware/software co-design methodology for Convolutional Neural Networks, where the classification section is managed through Resistive RAMs (RRAMs). To this aim, RRAM arrays are mounted onto the ArC TWO instrumentation board, which is interfaced to a laptop. A software Python front-end executes convolutional layers for feature extraction, generates stimuli for RRAMs, and controls the instrumentation board. As a proof of concept, handwritten digits classification is exhibited.

Artificial Intelligence (AI) is revolutionizing industries worldwide, delivering unprecedented productivity gains across diverse sectors, from healthcare to manufacturing. Recent advances in generative AI models have particularly accelerated innovation, enabling more efficient execution of complex tasks such as drug discovery, autonomous driving, and predictive maintenance. In the areas of electronics manufacturing: a sector crucial to the advancement of modern technologies, the impact of AI is profound, with the potential to transform every stage of the supply chain. This perspective investigates the role of AI in reshaping the electronics and semiconductor industries, exploring how it integrates into various stages of production and development. The approach to AI integration is structured and methodical, addressing both challenges and opportunities across five key nanotechnology areas: materials discovery, device design, circuit and system design, testing/verification, and modeling. In materials discovery, AI aids in identifying new, more efficient and sustainable materials. In device design, it enhances the functionality and integration of components. AI’s capabilities in circuit and system design enables more complex and precise electronic systems. During the testing and verification stage, AI contributes to more rigorous and faster testing processes, ensuring reliability before market release. Finally, in modeling, AI’s predictive capabilities allow for accurate simulations, crucial for anticipating performance under various scenarios. Each pillar of this electronics supply chain underscores AI’s ability to accelerate processes, optimize performance, and reduce costs. Supported by case studies of AI-driven breakthroughs, this perspective provides a comprehensive review of current AI applications across the entire electronic supply chain, illustrating improvements in yield and sustainable manufacturing practices.