High-Throughput Pattern Matching with CMOL FPGA Circuits: Case for Logic-in-Memory Computing

Abstract

In this paper, we propose a novel CMOS+MOLecular (CMOL) field-programmable gate array (FPGA) circuit architecture to perform massively parallel, high-throughput computations, which is especially useful for pattern matching tasks and multidimensional associative searches. In the new architecture, patterns are stored as resistive states of emerging nonvolatile memory nanodevices, while the analyzed data are streamed via CMOS subsystem. The main improvements over prior work offered by the proposed circuits are increased nanodevice utilization and, as a result, substantially higher throughput, which is demonstrated by a detailed analysis of the implementation of pattern matching task on the new architecture. For example, our estimates show that the proposed CMOL FPGA circuits based on the 22-nm CMOS technology and one crossbar layer with 22-nm nanowire half-pitch allows up to 12.5% average nanodevice utilization, i.e., the fraction of the devices turned to the high conductive state, as compared to a typical 0.1% of the original CMOL FPGA circuits. This in turn enables throughput close to 7.1 × 1016cm2 at 1 fJ/bit energy efficiency, for matching of 107 250-bit patterns stored locally on a 1 cm2 chip. These numbers are at least 2 orders of magnitude better throughput as compared to that of other state-of-the-art FPGA methods, and begin to approach ternary content-addressable memory-like performance at similar CMOS technology nodes. More generally, we argue that the proposed concept combines the versatility of reconfigurable architectures and density of the associative memories. It can be viewed as a very tight symbiotic integration of memory and logic functions for high-performance logic-in-memory computing.