1: Arm 64-bit CPUs for current and future smart cameras. It is also critical to highlight the advancements that Arm has been making on imaging and ML through extensions to Neon starting with SVE in the Armv8 architecture and SVE2 in Armv9 architecture.įig. One fused-MAC operation equates to 2 FLOPs. FLOPS are defined as fused-MAC operations that contain a multiply portion and an accumulate portion. The Armv8-A 64-bit architecture increases the FLOPS¹-per-cycle performance for single-precision by 2x and double-precision by 5x, which improves the user experience. 64-bit processors are well suited to meet these demands.įurthermore, to meet such a need for increasing performance and bandwidth requirements for imaging and ML workloads, smart cameras need support for floating point operations. The need for simultaneous 4K encoding from multiple streams further increases the demand for faster CPU performance. Higher fps will enable cameras to detect, identify, and recognize smaller objects and fast-moving objects with much better precision. Furthermore, if the frame rate needs to increase from 15fps to 30fps to 60fps to better identify objects, then the need for data rate and performance would continue to scale accordingly.
Moving from 1080p to 4K doubles the data rate of the incoming video streams from the cameras with the same encoding schemes. Imaging requirements are increasing, smart cameras are embracing 4K resolution in the low-end cameras and 8K resolution in the mid- and high-end cameras.
When the benefits of improved register support are combined with the increased memory map of 64-bit architectures, the software has direct access to more local data for processing, reducing need for the memory swapping, which impacts performance as data sets are swapped in and out of local memory. The increased register support also means that the developer can take advantage of the state of the art in compiler optimizations, which will further boost performance. The increase in both number and width of registers mean that larger data sets can be processed with a reduced number of memory accesses, resulting in faster data processing. Most notably, 64-bit CPU architectures come with enhanced register support and larger memory maps. Moving to 64-bit processing brings advancements in the CPU hardware, increasing performance, and efficiency.
With these advancements it will be possible to detect hundreds of people at a time or recognize license plates on speeding cars, all without needing to transmit large volumes of data to the cloud in real-time. And, as compute is increased and advances in AI use cases in smart cameras continue to scale, it is possible to run these workloads locally on the CPU. As an example, in smart cities across the world we see cameras capable of running AI algorithms to detect people, pets, packages, license plates, and other objects.
Put simply, to run advanced AI workloads at the edge or on the endpoint camera itself, there is a need for higher performance processing, to address applications such as high-definition imaging to enhanced security.įollowing the migration seen in laptops and smartphones, smart cameras are moving towards 64-bit processing to leverage more advanced capabilities and enable more leading-edge applications. Diverse and complex use cases leveraging artificial intelligence (AI) and machine learning (ML) are driving the demand for increased processing capabilities and, increasingly, it makes sense, for scalability if nothing else, to provide that compute in the camera itself. From smart cities that are safer and more efficient to rainforests that are monitored for illegal logging, the increasing need for advanced vision technology is growing. The future of smart camera technology brings with it profound transformations in the way we interact with each other and the world around us.