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Song H, Park W, Kim G, Choi MG, In JH, Rhee H, Kim KM. Memristive Explainable Artificial Intelligence Hardware. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2400977. [PMID: 38508776 DOI: 10.1002/adma.202400977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 03/11/2024] [Indexed: 03/22/2024]
Abstract
Artificial intelligence (AI) is often considered a black box because it provides optimal answers without clear insight into its decision-making process. To address this black box problem, explainable artificial intelligence (XAI) has emerged, which provides an explanation and interpretation of its decisions, thereby promoting the trustworthiness of AI systems. Here, a memristive XAI hardware framework is presented. This framework incorporates three distinct types of memristors (Mott memristor, valence change memristor, and charge trap memristor), each responsible for performing three essential functions (perturbation, analog multiplication, and integration) required for the XAI hardware implementation. Three memristor arrays with high robustness are fabricated and the image recognition of 3 × 3 testing patterns and their explanation map generation are experimentally demonstrated. Then, a software-based extended system based on the characteristics of this hardware is built, simulating a large-scale image recognition task. The proposed system can perform the XAI operations with only 4.32% of the energy compared to conventional digital systems, enlightening its strong potential for the XAI accelerator.
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Affiliation(s)
- Hanchan Song
- Department of Materials Science and Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Woojoon Park
- Department of Materials Science and Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Gwangmin Kim
- Department of Materials Science and Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Moon Gu Choi
- Department of Materials Science and Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Jae Hyun In
- Department of Materials Science and Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Hakseung Rhee
- Department of Materials Science and Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Kyung Min Kim
- Department of Materials Science and Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
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Song H, Lee MG, Kim G, Kim DH, Kim G, Park W, Rhee H, In JH, Kim KM. Fully Memristive Elementary Motion Detectors for a Maneuver Prediction. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2309708. [PMID: 38251443 DOI: 10.1002/adma.202309708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 01/09/2024] [Indexed: 01/23/2024]
Abstract
Insects can efficiently perform object motion detection via a specialized neural circuit, called an elementary motion detector (EMD). In contrast, conventional machine vision systems require significant computational resources for dynamic motion processing. Here, a fully memristive EMD (M-EMD) is presented that implements the Hassenstein-Reichardt (HR) correlator, a biological model of the EMD. The M-EMD consists of a simple Wye (Y) configuration, including a static resistor, a dynamic memristor, and a Mott memristor. The resistor and dynamic memristor introduce different signal delays, enabling spatio-temporal signal integration in the subsequent Mott memristor, resulting in a direction-selective response. In addition, a neuromorphic system is developed employing the M-EMDs to predict a lane-changing maneuver by vehicles on the road. The system achieved a high accuracy (> 87%) in predicting future lane-changing maneuvers on the Next Generation Simulation (NGSIM) dataset while reducing the computational cost by 92.9% compared to the conventional neuromorphic system without the M-EMD, suggesting its strong potential for edge-level computing.
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Affiliation(s)
- Hanchan Song
- Department of Materials Science and Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Min Gu Lee
- Department of Materials Science and Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Gwangmin Kim
- Department of Materials Science and Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Do Hoon Kim
- Department of Materials Science and Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Geunyoung Kim
- Department of Materials Science and Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Woojoon Park
- Department of Materials Science and Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Hakseung Rhee
- Department of Materials Science and Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Jae Hyun In
- Department of Materials Science and Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Kyung Min Kim
- Department of Materials Science and Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
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