![]() The talk gives an insight into the development process behind the benchmark suite, describing the benchmark selection process, some of the design choices made, and the benchmarks selected for this first iteration consisting of four ML tasks: small vocabulary keyword spotting, binary image classification, small image classification, and anomaly detection using machine operating sounds. This work presents the first version of tinyMLPerf, a suite of benchmarks developed by the tinyML community to be used to compare tinyML hardware and software systems. Yet, to foster innovation, it is necessary to provide a benchmark that is fair, replicable, robust and enjoys the support of the wider community a global community in which several EMEA players also contributed to the development of the first version of such a benchmark. Providing benchmarks that allow the comparison of different solutions is even more challenging due to the wide range of targeted applications, power budgets, model specific optimizations, innovative HW and SW designs, and toolchains. Tiny machine learning (tinyML) is driving enormous growth within the IoT industry, enabling data driven development and previously unseen levels of machine intelligence and autonomy of operation at the far edge.Įvaluating the performance of low-power solutions in such a fastly evolving space is already difficult given the large design space offering various performance-energy tradeoffs even for a single application. ![]() I will present examples of NI circuits, and demonstrate applications of NI processing systems to extreme-edge use cases, that require low power, local processing of the sensed data, and that cannot afford to connect to the cloud for running AI algorithms. Noisy) properties of their biological counterparts. This tutorial will present strategies derived from neuroscience for carrying out robust and low latency computation using electronic neural computing elements that share the same (analog, slow, and NI hardware systems implement the principles of computation observed in the nervous system by exploiting the physics of their electronic devices to directly emulate the biophysics of real neurons and synapses. Neuromorphic Intelligence (NI) aims to fill this gap by developing ultra-low power electronic circuits and radically different brain-inspired in-memory computing architectures. However, they still have serious shortcomings for use cases that require closed-loop interactions with the real-world.Ĭurrent AI systems are still not able to compete with biological ones in tasks that involve real-time processing of sensory data and decision making in complex and noisy settings. Artificial Intelligence (AI) and deep learning algorithms are revolutionizing our computing landscape, and have demonstrated impressive results in a wide range of applications.
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |