Neural networks empowered by customer instructions.
Over the last few years there has been an important shift from cloud-level to device-level AI processing. The ability to run AI/ML tasks becomes a must-have when selecting an SoC or MCU for IoT and IIoT applications.
Embedded devices are typically resource-constrained, making it difficult to run AI algorithms on embedded platforms. This paper looks at what could make it easier from a software and hardware point of view and how Codasip tools and IP help with that.
This whitepaper focuses on:
By Alexey Shchekin, Solutions Engineer
Read more here.
Disaggregation and the wind-down of Moore’s Law have changed everything.
Different interconnect standards and packaging options being readied for mass chiplet adoption.
Continued expansion in new and existing markets points to massive and sustained growth.
Aging equipment and rising demand are pushing up prices and slowing production.
Experts at the Table: Designing for context, and geopolitical impacts on a global supply chain.
Interest in this particular ISA is expanding, but the growth of other open-source hardware is less certain.
Nanosheets are likeliest option throughout this decade, with CFETs and other exotic structures possible after that.
Hybrid bonding opens up whole new level of performance in packaging, but it’s not the only improvement.
Why this is becoming a bigger issue, and what can be done to mitigate the effects.
Some 300mm tools are converted to 200mm; equipment prices and chip manufacturing costs are rising.
From low resistance vias to buried power rails, it takes multiple strategies to usher in 2nm chips.
Manufacturing 3D structures will require atomic-level control of what’s removed and what stays on a wafer.
Disaggregation and the wind-down of Moore’s Law have changed everything.
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