CAMA: Energy and Memory Efficient Automata Processing in Content-Addressable Memories
Abstract
Accelerating finite automata processing is critical for advancing real-time analytic in pattern matching, data mining, bioinformatics, intrusion detection, and machine learning. Recent in-memory automata accelerators leveraging SRAMs and DRAMs have shown exciting improvements over conventional digital designs. However, the bit-vector representation of state transitions used by all state-of-the-art (SOTA) designs is only optimal in processing worst-case completely random patterns, while a significant amount of memory and energy is wasted in running most real-world benchmarks. We present CAMA, a Content-Addressable Memory (CAM) enabled Automata accelerator for processing homogeneous non- deterministic finite automata (NFA). A radically different state representation scheme, along with co-designed novel circuits and data encoding schemes, greatly reduces energy, memory, and chip area for most realistic NFAs. CAMA is holistically optimized with the following major contributions: (1) a 16×256 8-transistor (8T) CAM array for state matching, replacing the 256×256 6T SRAM array or two 16×256 6T SRAM banks in state-of-the- art (SOTA) designs; (2) a novel encoding scheme that enables content searching within 8T SRAMs and adapts to different applications; (3) a reconfigurable and scalable architecture that improves efficiency on all tested benchmarks, without losing support for any NFA that’s compatible with SOTA designs; (4) an optimization framework that automates the choice of encoding schemes and maps a given NFA to the proposed hardware. Two versions of CAMA, one optimized for energy (CAMA-E) and the other for throughput (CAMA-T), are comprehensively evaluated in a 28nm CMOS process, and across 21 real-world and synthetic benchmarks. CAMA-E achieves 2.1×, 2.8×, and 2.04× lower energy than CA, 2-stride Impala, and eAP. CAMA-T shows 2.68×, 3.87× and 2.62× higher average compute density than 2-stride Impala, CA, and eAP. Both versions reduce the chip area required for the largest tested benchmark by 2.48× over CA, 1.91× over 2-stride Impala, and 1.78× over eAP.
Zhiyu Chen
PhD 2023, now at Apple
Dai Li
PhD 2021, now at Google
