The rapid advancement in genomic sequencing technologies has resulted in an explosion of data, creating substantial computational bottlenecks in DNA analysis workloads. Applications such as DNA classification are particularly impacted due to their reliance on intensive, large-scale pattern matching. Existing hardware accelerator and software solutions are increasingly unable to manage the scale and energy demands of these datasets, highlighting the need for architectures that can perform faster and more efficient pattern matching. To address these challenges, we propose NP-CAM: a data-optimized, CAMbased accelerator designed for parallel and energy-efficient DNA classification. NP-CAM harnesses a network-on-chip to implement a novel optimized indexing and CAM partitioning scheme that reduces the active search space, allowing significant scalability. We demonstrate results for NP-CAM on commodity 10T binary CAM cell designs. Our experimental evaluations show that NPCAM achieves a simultaneous$65 \times$improvement in sequence throughput and an over$173 \times$improvement in energy efficiency over state-of-the-art hardware solutions on existing small viral workloads. We go on to demonstrate feasibility for larger bacterial and fungal workloads, enabling scalable DNA classification in the era of large-scale genomic data.

While the tree-based machine learning (TBML) models exhibit superior performance compared to neural networks on tabular data and hold promise for energy-efficient acceleration using aCAM arrays, their ideal deployment on hardware with explicit exploitation of TBML structure and aCAM circuitry remains a challenging task. In this work, we present MonoSparse-CAM, a new CAM-based optimization technique that exploits TBML sparsity and monotonicity in CAM circuitry to further advance processing performance. Our results indicate that MonoSparse-CAM reduces energy consumption by upto to 28.56× compared to raw processing and by 18.51× compared to state-of-the-art techniques, while improving the efficiency of computation by at least 1.68×.

Content-Addressable Memory (CAM) circuits, distinguished by their ability to accelerate data retrieval through a direct content-matching function, are increasingly crucial in the era of AI and increasing data computation. With the rise of AI models, hardware matching and hashing capabilities become essential, underscoring the need for a comprehensive survey of this evolving technology. This survey explores various CAM types across circuit designs and technologies, highlighting contributions to fields such as Machine Learning and genomics. We review 37 CAM cell designs, focusing on emerging trends in area and energy efficiency, pivotal for next-generation computing. Furthermore, we discuss current challenges and suggest future research directions in CAM technology.

With the current COVID-19 pandemic, contact tracing and building occupancy tracking are key components of re-opening policies and quickly containing virus outbreaks. WiFiMon is a network-centric contact tracing method that uses enterprise WiFi networks logs for tracking devices and inferring building occupancy and building contact tracing reports in office and campus settings.