This is an interesting paper. Does not name BRN or AKIDA but when read you will see why partnering with ARM Cortex M4 is so very exciting:
A Brief Review of Deep Neural Network Implementations for ARM Cortex-M Processor
....System-On-Chip (SoC) devices are an attractive solution that, in addition to high processing capabilities, includes multiple peripheral devices that can be very helpful for the sophisticated requirements of deep-learning applications. Examples of manufacturers that develop AI integrated circuits for edge computing are Samsung, Texas Instruments, Qualcomm, and STM. Some of their recent products are briefly presented below.......
5. Conclusions
Deep learning and deep neural networks are emerging as promising solutions for solving complex problems. Solving complex problems requires high computational capabilities and memory resources, so are traditionally designed to run on a large computer system around specialized hardware. However, recent research shows that simple applications can benefit from the deep learning paradigm and their edge computing implementation as well. Edge computing is the solution to many real-world problems that need to be solved soon. For instance, the automotive industry is using and developing prototypes using state-of-the-art hardware and software solutions for autonomous driving. Once these prototypes prove their ability to solve problems, the systems will have to run on real-world cars. At that stage, cost is necessary to be competitive in the market, and, using high performance computing solutions, the cost is high. The edge computing paradigm must be prepared with efficient and low-cost solutions while meeting specific requirements such as functional safety. In this work, we provide a summary of what edge computing means in the context of low-cost/low-power applications. Here, the ARM Cortex-M processor represents one of the best possible candidates. More specifically, we summarize deep neural network implementations using ARM Cortex-M core-based microcontrollers. From the software perspective, the STM32Cube.AI support package, made available by STMicroelectronics for its 32-bit microcontroller series, represents one of the best freely available tools. Implementing deep neural networks on embedded devices, such as microcontrollers, is a difficult task. This is mainly due to the computation and memory footprint constrains. For this reason, it is observed that developers are forced to customize existing architectures or even develop from scratch innovative models that better suit embedded processors. Optimization techniques such as quantization, pruning, and distillation are constantly evolving to achieve higher performance, and they are enabling developers to introduce state-of-the-art models of increasing complexity to the embedded domain. Ultimately, using an optimized hardware combined with optimized deep neural network architectures leads to maximum energy efficient systems. Electronics 2022, 11, 2545 19 of 21 Future work proposes to extend the study to a wider family of ARM cores, including, for example, deep learning applications running on Cortex-A type processors or even specialized Arm Ethos-N series processors for machine learning
https://www.mdpi.com/2079-9292/11/16/2545/pdf?version=1660467458
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