How to Achieve High-Accuracy Keyword Spotting on Cortex-M Processors
January 24, 2018
It IS possible to optimize neural network architectures to fit within the memory and compute constraints of microcontrollers – without sacrificing accuracy. We explain how, and explore the potential of depthwise separable convolutional neural networks for implementing keyword spotting on Cortex-M processors.
Keyword spotting (KWS) is a critical component for enabling speech-based user interactions on smart devices. It requires real-time response and high accuracy to ensure a good user experience. Recently, neural networks have become an attractive choice for KWS architecture because of their superior accuracy compared to traditional speech-processing algorithms.
To read the full article, click here
Related Semiconductor IP
- Neuromorphic Processor IP
- Flexible Pixel Processor Video IP
- Neural Video Processor IP
- GPNPU Processor IP - 32 to 864TOPs
- ECC7 Elliptic Curve Processor for Prime NIST Curves
Related Blogs
- Enhanced ARM DesignStart eliminates upfront license fees for ARM Cortex-M0 and Cortex-M3 processors
- What will it take for FPGAs to become as ubiquitous as processors?
- How many people does it take to design an SoC? - Redux. Building brains with processors.
- Microprocessor Report publishes extremely interesting comparison of STMicroelectronics SPEAr-1300 and Xilinx Zynq ARM-based, dual core application processors
Latest Blogs
- Cadence Powers AI Infra Summit '25: Memory, Interconnect, and Interface Focus
- Integrating TDD Into the Product Development Lifecycle
- The Hidden Threat in Analog IC Migration: Why Electromigration rules can make or break your next tapeout
- MIPI CCI over I3C: Faster Camera Control for SoC Architects
- aTENNuate: Real-Time Audio Denoising