This article explains which form of DRAM memory is best for your SoC application, comparing DDR variants, types of DIMM, mobile and low-power versions, graphics memory and 3D stacks.
The computational and algorithmic demands made by computer vision systems highlight HLS' value for AI system development.
SoC suppliers building the key components for hyperscale data centres need access to the latest IP to handle functions such as PCIe, DDR5, cache coherency, NVMe SSDs, and the highest-bandwidth Ethernet implementations.
Application-specific processors can provide high performance for specialised tasks at low energy cost.
Optimizing the way in which machine learning algorithms are implemented in hardware will be a major differentiator for SoCs, especially for edge devices.
Antifuse-based OTP NVM is highly scalable, has the area efficiency to enable macros of megabit capacities, and offers low read power.
The basics of USB 3.2, how to implement it in an SoC, and how USB Type-C connectors and cables are used in USB 3.2 systems.
As AI becomes pervasive in computing applications, so too does the need for high-grade security in all levels of the system.
The proliferation of attacks against embedded systems is making designers realize that they need to do more to secure their products and ecosystems.
Machine-learning strategies for embedded vision are evolving so quickly that designers need access to flexible, heterogenous processor architectures that can adapt as the algorithms evolve.
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