ABR has designed an innovative chip, the ABR TSP (Time Series Processor - https://appliedbrainresearch.com/products/tsp/ ), for speech recognition, natural language processing, and any other time series data. The TSP has the goal of delivering cloud-quality time-series inference for use in sensor data processing and dialog systems implementations at the edge. For speech processing, ABR's TSP will run BERT-sized NLP models at less than 60 mW SoC level power. Device makers can deliver low-power, low-latency, low-cost, real-time voice interfaces that save costs over CPUs and GPUs. For the same price as a keyword spotter chip, the TSP can move speech interactions to natural conversation from keyword-spotting, increasing customer satisfaction. For IoT uses, the TSP delivers larger models comparable with CPUs and GPUs, for less cost and with more accuracy and lower latency than using an MPU. ABR's TSP implements ABR's patented Legendre Memory Unit (LMU) algorithm which enables smaller ASR and NLP models. We will discuss ABR's LMU algorithm and the uses of the TSP chip for edge NLP and ASR, with examples.

The edge and embedded AI market is diverse and distributed, where systems that adopted industry standards have accelerated growth and lowered the barrier to entry.

With the emergence of AI accelerators and systems, it is important to learn from what has worked in the embedded space and leverage standards that enable developers and applications, reduce risk, and accelerate time to market. In this talk, we will outline some of the challenges to AI system adoption and how Flex Logix is working to make AI customization and deployment easier.