Cache Automaton: Repurposing Caches for Automata Processing

Finite State Automata (FSA) are powerful computational models for extracting patterns from large streams (TBs/PBs) of unstructured data such as system logs, social media posts, emails, and news articles. FSA are also widely used in network security [6], bioinformatics [4] to enable efficient pattern matching. Compute-centric architectures like CPUs and GPG-PUs perform poorly on automata processing due to ir-regular memory accesses and can process only few state transitions every cycle due to memory bandwidth limitations. On the other hand, memory-centric architectures such as the DRAM-based Micron Automata Processor (AP) [2] can process up to 48K state transitions in a single cycle due to massive bit-level parallelism and reduced data movement/instruction processing overheads. Micron Automata Processor: The Micron AP re-purposes DRAM columns to store FSM states and the row address to stream input symbols. It implements homogeneous non-deterministic finite state automata (NFA), where each state has incoming transitions only on one input symbol. Each state has a label, which is the one-hot encoding of the symbols it is required to match against. Each input symbol is processed in two phases: (1) state-match, where the states whose label matches the input symbol are determined and (2) state-transition, where each of the matched states activates their corresponding next states. We explore SRAM-based last-level caches (LLCs) as a substrate for automata processing that are faster and integrated on processor dies. Cache capacity: One immediate concern is whether caches can store large automata. Interestingly, we observe that AP sacrifices a huge fraction of die area to accommodate the routing matrix and other non-memory components required for automata processing and only has a packing density comparable to caches. Repurposing caches for automata processing: While the memory technology benefits of moving to SRAM are apparent, repurposing the 40-60% passive LLC die area for massively parallel automata computation comes with several challenges. Processing an input symbol every LLC access (∼20-30 cycles @ 4GHz), would lead to an operating frequency comparable to DRAM-based AP (∼200 MHz), negating the memory technology benefits. Increasing operating frequency further can be made possible only by architecting an (1) in-situ computation model which is cognizant of internal geometry of LLC slices, and (2) accelerating state-match (array read) and state-transition (switch+wire propagation delay) phases of symbol processing. Accelerating state-match: This is challenging because industrial LLC subarrays typically have 4-8 bitlines sharing a sense-amp. This means that only 1 out of 4-8 states stored can match every cycle leading to gross under-utilization and loss of parallelism. To solve this, we leverage sense-amp cycling techniques that exploit spatial locality of state-matches. Accelerating state-transition: Accelerating state-transition at low-area cost requires the design of a scalable interconnect that efficiently encodes and supports multiple state-transitions on the same cycle, often to the same destination state. We observe that a 8T SRAM memory array can be re-purposed to become a compact state-transition crossbar for automata supporting large fan-in for states. Scaling to large automata: Supporting all-to-all connectivity between states requires prohibitively large and slow switches. Large real-world automata are typically composed of several connected components, grouped into densely connected partitions with only few (8-16) interconnections between them. This motivated us to explore a hierarchical switch topology with local switches providing rich intra-partition connectivity and global switches providing sparse inter-partition connectivity. To this end, we also design a compiler that automates the mapping of states into SRAM arrays. We propose and evaluate two architectures and mapping policies, one optimized for performance and the other optimized for space, across a set of 20 diverse benchmarks from ANMLZoo [5] and Regex [1] suites. We also demonstrate acceleration of parsing activities in browser front-end as a case study where FSA computations are a bottleneck taking up to 40% of the loading time of web pages [3]. The performance optimized and space optimized designs provide a speedup of 15× and 9× over Micron's AP respectively.