The year 2025 saw an accelerated artificial intelligence (AI) adoption in every foreseeable and unforeseeable facet of human life and profession. The next year will see increased pervasive adoption of physical AI.

The year 2024 was a prolific year for computer science and engineering standards activity: the IEEE Computer Society had more than 40 new standards solidified and approved, and another 75+ project authorization requests surfaced.

The IEEE Computer Society, along with the IEEE Standards Association and IEEE Reliability Society, is at the forefront of global standardization initiatives in reliable technologies, including predictive maintenance, as illustrated by IEEE P2851.1 and IEEE 1856-2017.

The past year was marked by an accelerated artificial intelligence (AI) megatrend, fueling three other megatrends marking the future to come: health care, robotics, and space.

At the intersection of the physical and digital worlds, the metaverse is driving a new technological revolution across industrial applications, entertainment and gaming, transportation, and numerous other sectors.

Silicon Lifecycle Management (SLM) has emerged as a strategic solution to address the challenges on meeting increasing silicon production quality and in-field dependability requirements. In this Innovation Practices track, we invited industry experts to discuss the advances in silicon lifecycle reliability, safety and security domains, respectively.

Semiconductor design, manufacturing, and system deployment are being challenged on many fronts owing to technology scaling, process variability, device aging effects, ever increasing performance expectations, and the continued reduction in time with respect to volume. Data centers and applications have stringent reliability, availability, and serviceability (RAS) requirements straining under the massive scale of compute today. Silent data corruption (SDC) has become a problem for all semiconductor suppliers in large-scale compute. Automotive OEMs (original equipment manufacturers) are accelerating adoption of advanced process nodes to address the compute required for fully autonomous transportation while still meeting the stringent functional safety (FuSa) requirements. In this paper, we describe the challenges, potential causes, and mitigation techniques to address modern RAS requirements including SDC. Silicon Lifecycle Management (SLM) will be explained which involves the insertion of in-chip monitors, Electronic Design Automation (EDA) tools, and data analytics solutions on the cloud, edge and embedded in the SoC. SLM monitors, collects, and stores device data throughout a system's life and it provides insights through purpose-built analytics for production data through the mission mode of operation.

Engaging professionals from all areas of computing, the IEEE Computer Society sets the standard for education and engagement that fuels global technological advancement.Through conferences, publications, and programs, IEEE CS empowers, guides, and shapes the future of its members, and the greater industry, enabling new opportunities to better serve our world.

As the 2024 President of the IEEE Computer Society, Jyotika Athavale’s passion for diversity, equity, and inclusion and inspiration for students and young professionals is evident in the countless initiatives that she spearheads across the global engineering community.

IEEE Computer Society President, Jyotika Athavale, kicks off the New Year with an overview of the Society’s priorities, and encourages members to make connections by engaging with the Society’s many activities and offerings.

Digital twins provide living digital models of physical systems that enable data-driven analysis and application of artificial intelligence to better manage selective aspects of the data center and drive efficiency for sustainability.

The widespread use of artificial intelligence (AI)-based systems has raised several concerns about their deployment in safety-critical systems. Industry standards, such as ISO26262 for automotive, require detecting hardware faults during the mission of the device. Similarly, new standards are being released concerning the functional safety of AI systems (e.g., ISO/IEC CD TR 5469). Hardware solutions have been proposed for the infield testing of the hardware executing AI applications; however, when used in applications such as Convolutional Neural Networks (CNNs) in image processing tasks, their usage may increase the hardware cost and affect the application performances. In this paper, for the very first time, a methodology to develop high-quality test images, to be interleaved with the normal inference process of the CNN application is proposed. An Image Test Library (ITL) is developed targeting the on-line test of GPU functional units. The proposed approach does not require changing the actual CNN (thus incurring in costly memory loading operations) since it is able to exploit the actual CNN structure. Experimental results show that a 6-image ITL is able to achieve about 95% of stuck-at test coverage on the floating-point multipliers in a GPU. The obtained ITL requires a very low test application time, as well as a very low memory space for storing the test images and the golden test responses.

The Functional Safety Standards Committee (FSSC) turned one year old. This article describes the FSSC results both in terms of sponsorship of IEEE Computer Society safety-related standards (for example, IEEE P2851) and of nurturing new initiatives.