Challenge: https://efabless.com/genai/challenges/4
In order to solve the problem of implementing Keyword Spotting (KWS) on very limited hardware, this project aims to solve this by implementing compact and optimized DSP blocks and scheduling around this.
Optimized low bit matrix multiplication with systolic arrays could be an option.
Expanded proposal utilizing AI...
The challenge of implementing Keyword Spotting (KWS) on extremely limited hardware resources poses a formidable task. However, this project is committed to overcoming this challenge by devising innovative solutions centered around compact and optimized Digital Signal Processing (DSP) blocks, strategically scheduled to maximize efficiency and performance.
At the heart of this endeavor lies the development of DSP blocks meticulously crafted for minimal footprint and maximal efficiency. These DSP blocks are engineered to handle crucial signal processing tasks essential for KWS, such as feature extraction, filtering, and pattern recognition. Through rigorous optimization and fine-tuning, these blocks are tailored to operate seamlessly within the constraints of the target hardware, ensuring optimal resource utilization without compromising on functionality or accuracy.
One promising avenue for achieving efficient KWS on limited hardware is through the utilization of low-bit matrix multiplication techniques augmented by systolic arrays. By harnessing the power of low-precision arithmetic and leveraging the parallelism inherent in systolic array architectures, we can significantly accelerate crucial computations involved in KWS while minimizing computational complexity and memory requirements. This approach not only enhances the overall speed and efficiency of the KWS system but also facilitates its deployment on resource-constrained platforms with stringent power and memory constraints.
In addition to developing compact and optimized DSP blocks, strategic scheduling plays a pivotal role in maximizing the performance of the KWS system on limited hardware. Through careful orchestration of tasks and resources, we aim to minimize idle time and maximize throughput, ensuring that every computational cycle is effectively utilized to advance the KWS process. By judiciously allocating resources and prioritizing critical tasks, we strive to achieve optimal performance and responsiveness, even within the confines of severely constrained hardware environments.
In conclusion, the journey towards implementing Keyword Spotting on very limited hardware is undoubtedly challenging, but through the innovative integration of compact and optimized DSP blocks, coupled with strategic scheduling techniques and leveraging advancements such as low-bit matrix multiplication with systolic arrays, we are poised to surmount these challenges and unlock the full potential of KWS on resource-constrained hardware platforms.