Author's Latest Posts


Always-on Face Unlock


Accurate face verification has long been considered a challenge due to the number of variables, ranging from lighting to pose and facial expression. This white paper looks at a new approach—combining classic and modern machine learning (deep learning) techniques—that achieves 98.36% accuracy, running efficiently on Arm ML-optimized platforms, and addressing key security issues such as mu... » read more

360-Degree Video Rendering


The introduction of virtual reality has brought new applications to the surface as well as derivatives of existing technologies. One improvement over existing technologies can be seen in the case of 360-degree video. The increased immersion of virtual reality can easily be applied to video—providing superior user experience over the traditional video that is projected into flat surfaces. T... » read more

Arm Enterprise Virtualization With Arm System IP, Backplane Integration And Performance


Virtualization has become ubiquitous across the infrastructure market, increasing efficiency and security, boosting productivity and reducing operating costs. However, system performance remains crucial to ensuring a virtualized environment does not affect the end user’s experience. Performance within this environment depends on a number of factors such as transaction bandwidth, latencies an... » read more

Foveated Rendering


Virtual Reality (VR) is becoming increasingly popular due to its ability to immerse the user into an experience. Those experiences can vary from watching a movie in a simulated theatre, having a look at your personal pictures as though they were paintings in a museum or finding yourself in front row seats of a huge sporting event. These specific experiences don’t stress the device hardware to... » read more

Cache Speculation Side-Channels


This whitepaper looks at the susceptibility of Arm implementations following research findings from security researchers, including Google and MIT, on new potential cache timing side-channels exploiting processor speculation. This paper also outlines possible mitigations that can be employed for software designed to run on existing Arm processors. To read more, click here. » read more

The New Voice Of The Embedded Intelligent Assistant


As intelligent assistance is becoming vital in our daily lives, the technology is taking a big leap forward. Recognition Technologies & Arm have published a white paper that provides technical insight into the architecture and design approach that’s making the gateway a more powerful, efficient place for voice recognition. Some topics covered include: Why knowing who is speaking is i... » read more

Mobile Machine Learning Hardware At Arm


Machine learning is playing an increasingly significant role in emerging mobile application domains such as AR/VR, ADAS, etc. Accordingly, hardware architects have designed customized hardware for machine learning algorithms, especially neural networks, to improve compute efficiency. However, machine learning is typically just one processing stage in complex end-to-end applications, which invol... » read more

Cache Speculation Side-Channels


Cache timing side-channels are a well understood concept in the area of security research. As such, this whitepaper will provide a simple conceptual overview rather than an in-depth explanation. The basic principle behind cache timing side-channels is that the pattern of allocations into the cache, and, in particular, which cache sets have been used for the allocation, can be determined by m... » read more

Optimizing Machine Learning Workloads On Power-Efficient Devices


Software frameworks for neural networks, such as TensorFlow, PyTorch, and Caffe, have made it easier to use machine learning as an everyday feature, but it can be difficult to run these frameworks in an embedded environment. Limited budgets for power, memory, and computation can all make this more difficult. At Arm, we’ve developed Arm NN, an inference engine that makes it easier to target di... » read more

Packing Neural Networks Into End-User Client Devices


Most of today’s neural networks can only run on high-performance servers. There’s a big push to change this and simplify network processing to the point where the algorithms can run on end-user client devices. One approach is to eliminate complexity by replacing floating-point representation with fixed-point representation. We take a different approach, and recommend a mix of the two, so as... » read more

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