Litcius/Paper detail

16.1 A 22nm 4Mb 8b-Precision ReRAM Computing-in-Memory Macro with 11.91 to 195.7TOPS/W for Tiny AI Edge Devices

Cheng-Xin Xue, Je-Min Hung, Hui-Yao Kao, Yen-Hsiang Huang, Sheng-Po Huang, Fu-Chun Chang, Peng Chen, Ta-Wei Liu, Chuan-Jia Jhang, Chin-I Su, Win-San Khwa, Chung‐Chuan Lo, Ren-Shuo Liu, Chih-Cheng Hsieh, Kea‐Tiong Tang, Yu-Der Chih, Tsung-Yung Jonathan Chang, Meng‐Fan Chang

2021176 citationsDOI

Abstract

Battery-powered tiny-AI edge devices require large-capacity nonvolatile compute-in-memory (nvCIM), with multibit input (IN), weight (W), and output (OUT) precision to support complex applications, high-energy efficiency (EF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MAC</sub> ), and short computing latency (t <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AC</sub> ) for multiply-and-accumulate (M <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AC</sub> ) operations. Due to the low read-disturb-free voltage of nonvolatile memory (NVM) devices and the large parasitic load on the bitline, most existing Mb-level nvCIM macros use a current-mode read scheme [1-5] and only achieve a low IN-W precision (binary to 4b).

Topics & Concepts

MacroResistive random-access memoryNon-volatile memoryComputer scienceLatency (audio)Enhanced Data Rates for GSM EvolutionComputer hardwareBinary numberElectrical engineeringVoltageArtificial intelligenceEngineeringArithmeticTelecommunicationsMathematicsProgramming languageAdvanced Memory and Neural ComputingFerroelectric and Negative Capacitance DevicesSemiconductor materials and devices