Design exploration of ASIP architectures for the K-Nearest Neighbor machine-learning algorithm

Increasingly, machine-learning algorithms are playing an important role in the context of embedded and real-time systems. Applications such as wireless sensor networks, security, and commercial enterprises are increasingly relying on machine-learning algorithms to efficiently make predictive decisions based on the large volumes of data these systems collect. Therefore, there is a need to accelerate the runtime of these algorithms, especially for real-time applications. In this paper, we propose several Application Specific Instruction Processor (ASIP) architectures for the K-Nearest Neighbor (KNN) classification algorithm. Each ASIP is developed using Cadence Tensilica tools and represents a tightly-coupled architecture. Our experimental results, based on several benchmarks, show that proposed ASIPs achieve speedups of 86×-650× over the original software implementation.