The heterogeneous integration roadmap (HIR) is an ongoing initiative of the IEEE Electronics Packaging Society. It’s a living document that continues to evolve and expand in response to technological developments like the growth of generative artificial intelligence (AI) and quantum computing. This FAQ starts with a brief overview of heterogeneous integration, looks at the scope […]
FAQ
How does the open domain-specific architecture relate to chiplets and generative AI?
The Open Domain-Specific Architecture (ODSA) is a project within the Open Compute Project (OCP) community to establish open physical and logical die-to-die (D2D) interfaces for chiplets. The goal is to democratize the design and use of chiplets for domain-specific high-performance computing (HPC) applications like generative artificial intelligence (AI). Domain-specific architectures (DSAs) are an emerging approach […]
How do heterogeneous integration and chiplets support generative AI?
Chiplets are here, and more are coming. They can overcome the yield limitations of large ASICs, support a mix-and-match strategy for heterogeneous semiconductor IPs and multiple process nodes, improve thermal performance, and speed time to market. They are being used in a range of high-performance computing (HPC) applications, notably generative artificial intelligence (AI) and machine […]
Securing devices for the IoT – FIPS 140-3 and common criteria
For developers of IoT devices and networks, the Federal Information Processing Standard (FIPS) 140-3 and the Common Criteria (CC) serve different but complementary functions. FIPS 140-3 is designed for validating software and hardware in cryptographic modules, while CC is designed to evaluate security functions in IT software and hardware. In both cases, there are related […]
Securing devices for the IoT — IEC 62443, SESIP, and PSA
The International Electrotechnical Commission (IEC) 62443 series of standards is focused on security for Industrial Automation and Control Systems (IACS); the Security Evaluation Standard for IoT Platforms (SESIP) provides a method for trustworthy assessment of the security IoT platforms, including the industrial IoT (IIoT), and Platform Security Architecture (PSA) certification requires strict adherence to the […]
Securing devices for the IoT — managing memory
Memory management is important in all digital electronic devices, including devices designed for use on the Internet of Things (IoT). It supports efficient resource allocation and memory utilization and prevents memory fragmentation, improving efficiency. It also supports memory protection and device security. This FAQ briefly reviews the common weakness enumeration (CWE) scheme for identifying software […]
Securing devices for the IoT – firmware, software, and OTA
Over-the-air (OTA) updating of firmware and software on Internet of Things (IoT) devices is a requirement, not an option. The large number of IoT devices makes manual updates impractical and even dangerous. This FAQ reviews two key tools for managing and implementing OTA for IoT devices: open-source standards for managing resource-constrained devices and industry standards […]
Securing devices for the IoT — minimize the attack surface
An attack surface is the sum of all the attack vectors or ways an attacker can gain malicious access to a network or system. The growth in remote work and the adoption of cloud services have increased the number of attack vectors and the attack surface size for most organizations and applications. This FAQ reviews […]
Matter 1.2 is here — what does that mean for the smart home?
by Rob Alexander, Principal Product Manager for Matter at Silicon Labs The rapid evolution and expansion of the IoT over the last decade has resulted in “smart” devices becoming a pervasive part of our everyday lives. Thousands of smart home products exist on the market today, and everything from our light switches to our vacuum cleaners now […]
What are the four types of machine learning, and what are they used for?
Machine learning (ML) is a subset of artificial intelligence (AI). It’s used to enable machines to independently improve their performance using data and experience to modify future actions. ML applications range from autonomous vehicles to business processes. Various approaches to ML use different learning styles, require different levels of data input, and are optimized for […]
