GCOC: A Genome Classifier-On-Chip Based on Similarity Search Content Addressable Memory

Area of Science:

• Genomics
• Bioinformatics
• Computer Engineering

Background:

• Genome classification is crucial for biological research and diagnostics.
• Existing methods often lack the speed and efficiency required for real-time, portable applications.
• K-mer matching is a common technique for sequence comparison.

Purpose of the Study:

• To develop and evaluate a novel system-on-chip (SoC) for efficient genome classification.
• To assess the performance, power consumption, and silicon area of the GCOC platform.
• To demonstrate the feasibility of real-time, portable DNA classification.

Main Methods:

• Designed and manufactured a genome classification system-on-chip (GCOC) using a 65nm process.
• Utilized k-mer matching with a similarity, or approximate search-capable Content Addressable Memory (SAS-CAM) for classification.
• Controlled classification operations with an embedded RISC-V processor.

Main Results:

• GCOC achieves a classification speed of 769.2K short DNA reads per second.
• The SoC has a silicon area of 3.12 mm² and a power consumption of 1.27 mW.
• The system supports both exact and approximate (Hamming distance tolerant) k-mer matching.

Conclusions:

• The GCOC platform offers a highly efficient and energy-saving solution for genome classification.
• Its performance characteristics make it suitable for deployment in portable, real-time applications, such as point-of-care diagnostics.
• The developed SoC addresses the need for fast, easy-to-operate, and low-power genomic analysis tools.