Monthly Archives: June 2024

Integrating Exception level and trust zones as SdV foundation

ARM Exception Levels (ELs) define a hierarchy of privilege levels for code execution, controlling access to system resources: TrustZone technology creates isolated execution environments within the processor, dividing the system into two worlds: The integration of Exception Levels and TrustZones … Continue reading

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SeL4 in a nutshell

In a microkernel-based system, device drivers operate as regular processes in user mode, each within its own address space. This separation is enforced using hardware mechanisms such as the memory-management unit (MMU) and the IOMMU, which handle devices performing direct … Continue reading

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ARM TrustZone architecture in a nutshell

ARM TrustZone-enabled processors allow secure and non-secure code to run separately on the same physical core by utilising a form of virtualization to create two distinct environments. This ensures robust security without depending on the OS. In Armv8-M systems, the … Continue reading

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May KAN will be the next AI disruption step

KANs (external link) revolutionize traditional architecture by swapping out weight parameters for univariate function parameters. Utilizing multiple variables like B-splines, KANs create smooth, adjustable curves. Nodes sum these outputs without nonlinear transformations, enhancing accuracy. The secret lies in adaptive univariate … Continue reading

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Fine tuning as playfield

In the previous post, I discussed how training a state-of-the-art model from scratch is unaffordable for most companies. Meanwhile, Apple announced their AI strategy, showcasing how small models, like the 7 billion parameter model, can be personalised and adapted on … Continue reading

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A cup of TEE

ARM TrustZone is ARM’s solution for creating a Trusted Execution Environment (TEE). It divides the SoC system into two worlds: a secure (S) world and a normal one (N). A key feature ARM implemented for TrustZone is the propagation of … Continue reading

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Potential for a next AI Winter

In the 1940s, Walter Pitts and Warren McCulloch revolutionised computing by showing that artificial neurons could perform basic logical functions. This concept was advanced in 1957 by Frank Rosenblatt with his perceptron, capable of learning from data. However, in 1969, … Continue reading

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From transformer networks and decision making methods

Multi-attribute decision-making (MADM) methods are essential tools for evaluating, ranking, and selecting options based on various criteria. Surprisingly, many of us use these techniques intuitively, without even realizing the complex math behind them. Imagine you have three alternatives and three … Continue reading

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