A key challenge our partners are consistently looking to solve is: How can we continue to push performance boundaries, with maximum efficiency, while managing costs associated with manufacturing and yield? Today, as the ever more complex AI-accelerated computing landscape evolves, a key solution emerging is chiplets. Chiplets are designed to be combined to create larger and more complex systems that can be packaged and sold as a single solution, made of a number of smaller dice instead of one single larger monolithic die. This creates interesting new design possibilities, with one of the most exciting being a potential route to custom silicon for manufacturers who historically chose off-the-shelf solutions. This talk will describe two complementary approaches to realising this chiplet opportunity: · Decomposing an existing system across multiple chiplets, in the same way a monolithic chip is composed of IP blocks. · Aggregating well-defined peripherals across a motherboard into a single package. Both of these approaches require collaboration in standards to align on the many non-differentiating choices in chiplet partitioning. This talk will describe the standards framework that Arm is building with our partners, and the broader industry. Including, own specifications such as the Arm Chiplet System Architecture (Arm CSA), AMBA chip-to-chip and the role of industry standards such as UCIe.

The field of integrated photonics holds great promise for overcoming societal challenges in data and telecom, autonomous driving and healthcare in terms of cost, performance, and scalability. Similar to the semiconductor industry, the ever-increasing demands of various applications are driving the necessity for platform integration in photonics as well, enabling seamless integration of diverse functionalities into compact and efficient photonic devices. This high level of integration reduces footprint and drives down system level costs. In this trend towards high levels of integration , Indium Phosphide (InP) is the material of choice for long-distance communication lasers, owing to its proven track record over several decades. Leveraging standardized fabrication processes, the cost and performance targets can be addressed. The key advantages of InP-based integration lie in its ability to fully integrate lasers, amplifiers, modulators, and passives, providing a flexible and reliable platform for building complex Photonic Integrated Circuits (PICs). This paper will address the photonic integration platforms, the applicability to current and future markets requiring the need for further heterogenous integration with other technologies, and the change to a foundry business model, much like its electronics counterpart.

As artificial intelligence (AI) transforms various industries, state-of-the-art models have exploded in size and capability. The growth in AI model complexity is rapidly outstripping hardware evolution, making the deployment of these models on edge devices remain challenging. To enable advanced AI locally, models must be optimized for fitting into the hardware constraints. In this presentation, we will first discuss how computing hardware designs impact the effectiveness of commonly used AI model optimizations for efficiency, including techniques like quantization and pruning. Additionally, we will present several methods, such as hardware-aware quantization and structured pruning, to demonstrate the significance of software/hardware co-design. We will also demonstrate how these methods can be understood via a straightforward theoretical framework, facilitating their seamless integration in practical applications and their straightforward extension to distributed edge computing. At the conclusion of our presentation, we will share our insights and vision for achieving efficient and robust AI at the edge.

Mathematical models can yield certainty, as can probabilistic models where the probabilities degenerate. The field of formal methods emphasizes developing such certainty about engineering designs. In safety critical systems, such certainty is highly valued and, in some cases, even required by regulatory bodies. But achieving reasonable performance for sufficiently complex environments appears to require the use of AI technologies, which resist such certainty. This extended abstract suggests that certainty and intelligence may be fundamentally incompatible.

In our computing-driven era, the escalating power consumption of modern data centers, currently constituting approximately 3% of global energy use, is a burgeoning concern. With the anticipated surge in usage accompanying widespread adoption of AI technologies, addressing this issue becomes imperative. This keynote discusses a potential solution: integrating data centers into grid programs such as “demand response”. This strategy not only augments power usage without necessitating new fossil-fuel infrastructure, but also facilitates more ambitious renewable deployment. However, the unique scale, operational constraints, and future projections of data centers present distinct and urgent challenges for implementing demand response. On the other hand, data centers, in contrast to other electricity consumers, boast greater flexibility in power control and offer the potential for collaborative optimization. This intersection of challenges and capabilities opens avenues for designing intelligent solutions that dynamically adjust data center power usage in response to grid requirements while meeting performance demands.

This keynote delves into the opportunities as well as the myths inherent in this perspective on improving data center sustainability. While obstacles such as creating requisite software infrastructure, establishing institutional trust, and addressing privacy concerns are prominent, the landscape is evolving. Noteworthy achievements have emerged in the development of intelligent solutions that can be swiftly implemented in data centers to accelerate demand response. These multifaceted solutions encompass dynamic power capping, load scheduling, load forecasting, market bidding, and collaborative optimization. This keynote offers insights into the exciting journey towards making sustainable computing a reality.

- Unfortunately, the initial keynote presentation "RESPONSIBLE ARTIFICIAL INTELLIGENCE SYSTEMS: FROM TRUSTWORTHINESS TO GOVERNANCE" by Francisco Herrera (University of Granada, ES) cannot take place as planned and will be substituted by Marilyn Wolf¹ and William Widen² (¹University of Nebraska, US; ²University of Nebraska – Lincoln, US):

This talk will consider engineering methods for the evaluation of the safety of AI-enabled vehicles. We do not yet have theories and models for AI systems equivalent to those used to guide software/hardware verification and manufacturing test. However, engineers can adapt existing methods to help provide some assurance as to the safety and effectiveness of AI-enabled vehicles. We will also consider the role of management in the monitoring of validation for AI-enabled vehicles.