This paper proposes a novel Hamming distance tolerant content-addressable memory (HD-CAM) for energy-efficient in-memory approximate matching applications. HD-CAM exploits NOR-type based static associative memory bitcells, where we add circuitry to enable approximate search with programmable tolerance. HD-CAM implements approximate search using matchline charge redistribution rather than its rise or fall time, frequently employed in state-of-the-art solutions. HD-CAM was designed in a 65 nm 1.2 V CMOS technology and evaluated through extensive Monte Carlo simulations. Our analysis shows that HD-CAM supports robust operation under significant process variations and changes in the design parameters, enabling a wide range of mismatch threshold (tolerable Hamming distance) levels and pattern lengths. HD-CAM was functionally evaluated for virus DNA classification, which makes HD-CAM suitable for hardware acceleration of genomic surveillance of viral outbreaks, such as Covid-19 pandemics.
We propose a novel edit distance-tolerant content addressable memory (EDAM) for energy-efficient approximate search applications. Unlike state-of-the-art approximate search solutions that tolerate certain Hamming distance between the query pattern and the stored data, EDAM tolerates edit distance, which makes it especially efficient in applications such as text processing and genome analysis. EDAM was designed using a commercial 65 nm 1.2 V CMOS technology and evaluated through extensive Monte Carlo simulations, while considering different process corners. Simulation results show that EDAM can achieve robust approximate search operation with a wide range of edit distance threshold levels. EDAM is functionally evaluated as a pathogen DNA detection and classification accelerator. EDAM achieves up to 1.7× higher 𝐹 1 score for high-quality DNA reads and up to 19.55× higher 𝐹 1 score for DNA reads with 15% error rate, compared to state-of-the-art DNA classification tool Kraken2. Simulated at 667 MHz, EDAM provides 1, 214× average speedup over Kraken2. This makes EDAM suitable for hardware acceleration of genomic surveillance of outbreaks, such as the ongoing Covid-19 pandemic.
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