Pub Date : 2024-11-05DOI: 10.1038/s44335-024-00014-0
Maxwell Aifer, Kaelan Donatella, Max Hunter Gordon, Samuel Duffield, Thomas Ahle, Daniel Simpson, Gavin Crooks, Patrick J. Coles
Linear algebra is central to many algorithms in engineering, science, and machine learning; hence, accelerating it would have tremendous economic impact. Quantum computing has been proposed for this purpose, although the resource requirements are far beyond current technological capabilities. We consider an alternative physics-based computing paradigm based on classical thermodynamics, to provide a near-term approach to accelerating linear algebra. At first sight, thermodynamics and linear algebra seem to be unrelated fields. Here, we connect solving linear algebra problems to sampling from the thermodynamic equilibrium distribution of a system of coupled harmonic oscillators. We present simple thermodynamic algorithms for solving linear systems of equations, computing matrix inverses, and computing matrix determinants. Under reasonable assumptions, we rigorously establish asymptotic speedups for our algorithms, relative to digital methods, that scale linearly in matrix dimension. Our algorithms exploit thermodynamic principles like ergodicity, entropy, and equilibration, highlighting the deep connection between these two seemingly distinct fields, and opening up algebraic applications for thermodynamic computers.
{"title":"Thermodynamic linear algebra","authors":"Maxwell Aifer, Kaelan Donatella, Max Hunter Gordon, Samuel Duffield, Thomas Ahle, Daniel Simpson, Gavin Crooks, Patrick J. Coles","doi":"10.1038/s44335-024-00014-0","DOIUrl":"10.1038/s44335-024-00014-0","url":null,"abstract":"Linear algebra is central to many algorithms in engineering, science, and machine learning; hence, accelerating it would have tremendous economic impact. Quantum computing has been proposed for this purpose, although the resource requirements are far beyond current technological capabilities. We consider an alternative physics-based computing paradigm based on classical thermodynamics, to provide a near-term approach to accelerating linear algebra. At first sight, thermodynamics and linear algebra seem to be unrelated fields. Here, we connect solving linear algebra problems to sampling from the thermodynamic equilibrium distribution of a system of coupled harmonic oscillators. We present simple thermodynamic algorithms for solving linear systems of equations, computing matrix inverses, and computing matrix determinants. Under reasonable assumptions, we rigorously establish asymptotic speedups for our algorithms, relative to digital methods, that scale linearly in matrix dimension. Our algorithms exploit thermodynamic principles like ergodicity, entropy, and equilibration, highlighting the deep connection between these two seemingly distinct fields, and opening up algebraic applications for thermodynamic computers.","PeriodicalId":501715,"journal":{"name":"npj Unconventional Computing","volume":" ","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44335-024-00014-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-05DOI: 10.1038/s44335-024-00012-2
Adam M. Krajewski, Allison M. Beese, Wesley F. Reinhart, Zi-Kui Liu
Diverse disciplines across science and engineering deal with problems related to compositions, which exist in non-Euclidean simplex spaces, rendering many standard tools inaccurate or inefficient. This work explores such spaces conceptually in the context of materials discovery, quantifies their computational feasibility, and implements several essential methods specific to simplex spaces through a new high-performance open-source library nimplex. Most significantly, we derive and implement an algorithm for constructing a novel n-dimensional simplex graph data structure, containing all discretized compositions and possible neighbor-to-neighbor transitions. Critically, no distance or neighborhood calculations are performed, instead leveraging pure combinatorics and order in procedurally generated simplex grids, keeping the algorithm $${mathcal{O}}(N)$$ , with minimal memory, enabling rapid construction of graphs with billions of transitions in seconds. Additionally, we demonstrate how such graph representations can be combined to homogeneously express complex path-planning problems, while facilitating efficient deployment of existing high-performance gradient descent, graph traversal, and other optimization algorithms.
科学和工程领域的多个学科都在处理与组合相关的问题，这些问题存在于非欧几里得单纯形空间中，导致许多标准工具不准确或效率低下。这项研究从概念上探索了材料发现背景下的这类空间，量化了其计算可行性，并通过一个新的高性能开源库 nimplex 实现了几种针对单纯形空间的基本方法。最重要的是，我们推导并实现了一种构建新颖的 n 维单纯形图数据结构的算法，其中包含所有离散化组合和可能的邻域到邻域转换。重要的是，该算法不进行距离或邻域计算，而是利用纯粹的组合学和程序化生成的单纯形网格中的秩序，保持算法 $${mathcal{O}}(N)$$ 的最小内存，从而能够在数秒内快速构建具有数十亿次转换的图形。此外，我们还展示了如何将这种图表示法结合起来，同质地表达复杂的路径规划问题，同时促进现有高性能梯度下降、图遍历和其他优化算法的高效部署。
{"title":"Efficient generation of grids and traversal graphs in compositional spaces towards exploration and path planning","authors":"Adam M. Krajewski, Allison M. Beese, Wesley F. Reinhart, Zi-Kui Liu","doi":"10.1038/s44335-024-00012-2","DOIUrl":"10.1038/s44335-024-00012-2","url":null,"abstract":"Diverse disciplines across science and engineering deal with problems related to compositions, which exist in non-Euclidean simplex spaces, rendering many standard tools inaccurate or inefficient. This work explores such spaces conceptually in the context of materials discovery, quantifies their computational feasibility, and implements several essential methods specific to simplex spaces through a new high-performance open-source library nimplex. Most significantly, we derive and implement an algorithm for constructing a novel n-dimensional simplex graph data structure, containing all discretized compositions and possible neighbor-to-neighbor transitions. Critically, no distance or neighborhood calculations are performed, instead leveraging pure combinatorics and order in procedurally generated simplex grids, keeping the algorithm $${mathcal{O}}(N)$$ , with minimal memory, enabling rapid construction of graphs with billions of transitions in seconds. Additionally, we demonstrate how such graph representations can be combined to homogeneously express complex path-planning problems, while facilitating efficient deployment of existing high-performance gradient descent, graph traversal, and other optimization algorithms.","PeriodicalId":501715,"journal":{"name":"npj Unconventional Computing","volume":" ","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44335-024-00012-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-03DOI: 10.1038/s44335-024-00010-4
Manuel Le Gallo, Oscar Hrynkevych, Benedikt Kersting, Geethan Karunaratne, Athanasios Vasilopoulos, Riduan Khaddam-Aljameh, Ghazi Sarwat Syed, Abu Sebastian
Analog in-memory computing (AIMC) leverages the inherent physical characteristics of resistive memory devices to execute computational operations, notably matrix-vector multiplications (MVMs). However, executing MVMs using a single-phase reading scheme to reduce latency necessitates the simultaneous application of both positive and negative voltages across resistive memory devices. This degrades the accuracy of the computation due to the dependence of the device conductance on the voltage polarity. Here, we demonstrate the realization of a 4-quadrant MVM in a single modulation by developing analog and digital calibration procedures to mitigate the conductance polarity dependence, fully implemented on a multi-core AIMC chip based on phase-change memory. With this approach, we experimentally demonstrate accurate neural network inference and similarity search tasks using one or multiple cores of the chip, at 4 times higher MVM throughput and energy efficiency than the conventional four-phase reading scheme.
{"title":"Demonstration of 4-quadrant analog in-memory matrix multiplication in a single modulation","authors":"Manuel Le Gallo, Oscar Hrynkevych, Benedikt Kersting, Geethan Karunaratne, Athanasios Vasilopoulos, Riduan Khaddam-Aljameh, Ghazi Sarwat Syed, Abu Sebastian","doi":"10.1038/s44335-024-00010-4","DOIUrl":"10.1038/s44335-024-00010-4","url":null,"abstract":"Analog in-memory computing (AIMC) leverages the inherent physical characteristics of resistive memory devices to execute computational operations, notably matrix-vector multiplications (MVMs). However, executing MVMs using a single-phase reading scheme to reduce latency necessitates the simultaneous application of both positive and negative voltages across resistive memory devices. This degrades the accuracy of the computation due to the dependence of the device conductance on the voltage polarity. Here, we demonstrate the realization of a 4-quadrant MVM in a single modulation by developing analog and digital calibration procedures to mitigate the conductance polarity dependence, fully implemented on a multi-core AIMC chip based on phase-change memory. With this approach, we experimentally demonstrate accurate neural network inference and similarity search tasks using one or multiple cores of the chip, at 4 times higher MVM throughput and energy efficiency than the conventional four-phase reading scheme.","PeriodicalId":501715,"journal":{"name":"npj Unconventional Computing","volume":" ","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44335-024-00010-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Similarity search is essential in current artificial intelligence applications and widely utilized in various fields, such as recommender systems. However, the exponential growth of data poses significant challenges in search time and energy consumption on traditional digital hardware. Here, we propose a software-hardware co-optimization to address these challenges. On the software side, we employ a learning-to-hash method for vector encoding and achieve an approximate nearest neighbor search by calculating Hamming distance, thereby reducing computational complexity. On the hardware side, we leverage the resistance random-access memory crossbar array to implement the hash encoding process and the content-addressable memory with an in-memory computing paradigm to lower the energy consumption during searches. Simulations on the MovieLens dataset demonstrate that the implementation achieves comparable accuracy to software and reduces energy consumption by 30-fold compared to traditional digital systems. These results provide insight into the development of energy-efficient in-memory search systems for edge computing.
{"title":"In-memory search with learning to hash based on resistive memory for recommendation acceleration","authors":"Fei Wang, Woyu Zhang, Zhi Li, Ning Lin, Rui Bao, Xiaoxin Xu, Chunmeng Dou, Zhongrui Wang, Dashan Shang","doi":"10.1038/s44335-024-00009-x","DOIUrl":"10.1038/s44335-024-00009-x","url":null,"abstract":"Similarity search is essential in current artificial intelligence applications and widely utilized in various fields, such as recommender systems. However, the exponential growth of data poses significant challenges in search time and energy consumption on traditional digital hardware. Here, we propose a software-hardware co-optimization to address these challenges. On the software side, we employ a learning-to-hash method for vector encoding and achieve an approximate nearest neighbor search by calculating Hamming distance, thereby reducing computational complexity. On the hardware side, we leverage the resistance random-access memory crossbar array to implement the hash encoding process and the content-addressable memory with an in-memory computing paradigm to lower the energy consumption during searches. Simulations on the MovieLens dataset demonstrate that the implementation achieves comparable accuracy to software and reduces energy consumption by 30-fold compared to traditional digital systems. These results provide insight into the development of energy-efficient in-memory search systems for edge computing.","PeriodicalId":501715,"journal":{"name":"npj Unconventional Computing","volume":" ","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44335-024-00009-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142360088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-12DOI: 10.1038/s44335-024-00011-3
Wei D. Lu, Christof Teuscher, Stephen A. Sarles, Yuchao Yang, Aida Todri-Sanial, Xiao-Bo Zhu
{"title":"A perfect storm and a new dawn for unconventional computing technologies","authors":"Wei D. Lu, Christof Teuscher, Stephen A. Sarles, Yuchao Yang, Aida Todri-Sanial, Xiao-Bo Zhu","doi":"10.1038/s44335-024-00011-3","DOIUrl":"10.1038/s44335-024-00011-3","url":null,"abstract":"","PeriodicalId":501715,"journal":{"name":"npj Unconventional Computing","volume":" ","pages":"1-2"},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44335-024-00011-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142170398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-02DOI: 10.1038/s44335-024-00008-y
Joao Henrique Quintino Palhares, Nikhil Garg, Pierre-Antoine Mouny, Yann Beilliard, J. Sandrini, F. Arnaud, Lorena Anghel, Fabien Alibart, Dominique Drouin, Philippe Galy
Seeking to circumvent conventional computing bottlenecks, hardware alternatives, from brain-inspired designs to cryogenic quantum systems, necessitate integrating emerging non-volatile memories. Yet, the immaturity and unreliability of cryogenic-compatible memories hinder scalable computing advancements. This study characterizes 28 nm FD-SOI substrate-embedded Ge-rich Ge2Sb2Te5 phase change memories (ePCMs) down to 12 K to overcome these hurdles. It reveals that ePCMs is cryogenic compatible and can encode multiple resistance states with minimal drift, essential for advanced computing solutions. Through simulations, the ePCM’s impact on a spiking neural network (SNN) performing MNIST classification is evaluated. The SNN maintains high accuracy for extended periods of 2 years at cryogenic temperatures, while an accuracy drop of 10.8% is observed at room temperature. These results highlight the potential of multilevel ePCMs in brain-inspired cryogenic computing applications, offering a promising avenue for the evolution of unconventional computing systems.
{"title":"28 nm FDSOI embedded PCM exhibiting near zero drift at 12 K for cryogenic SNNs","authors":"Joao Henrique Quintino Palhares, Nikhil Garg, Pierre-Antoine Mouny, Yann Beilliard, J. Sandrini, F. Arnaud, Lorena Anghel, Fabien Alibart, Dominique Drouin, Philippe Galy","doi":"10.1038/s44335-024-00008-y","DOIUrl":"10.1038/s44335-024-00008-y","url":null,"abstract":"Seeking to circumvent conventional computing bottlenecks, hardware alternatives, from brain-inspired designs to cryogenic quantum systems, necessitate integrating emerging non-volatile memories. Yet, the immaturity and unreliability of cryogenic-compatible memories hinder scalable computing advancements. This study characterizes 28 nm FD-SOI substrate-embedded Ge-rich Ge2Sb2Te5 phase change memories (ePCMs) down to 12 K to overcome these hurdles. It reveals that ePCMs is cryogenic compatible and can encode multiple resistance states with minimal drift, essential for advanced computing solutions. Through simulations, the ePCM’s impact on a spiking neural network (SNN) performing MNIST classification is evaluated. The SNN maintains high accuracy for extended periods of 2 years at cryogenic temperatures, while an accuracy drop of 10.8% is observed at room temperature. These results highlight the potential of multilevel ePCMs in brain-inspired cryogenic computing applications, offering a promising avenue for the evolution of unconventional computing systems.","PeriodicalId":501715,"journal":{"name":"npj Unconventional Computing","volume":" ","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44335-024-00008-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142117949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-02DOI: 10.1038/s44335-024-00007-z
Shiva Asapu, Taehwan Moon, Krishnamurthy Mahalingam, Kurt G. Eyink, James Nicolas Pagaduan, Ruoyu Zhao, Sabyasachi Ganguli, Reika Katsumata, Qiangfei Xia, R. Stanley Williams, J. Joshua Yang
We have measured the dynamical response of ZrO2 capacitors to applied triangular voltage waveforms with varying frequencies and amplitudes to determine the voltage and charge on the devices as a function of time. We have fit our experimental results to a Landau–Khalatnikov dynamical equation with a sixth order Landau–Ginzburg–Devonshire polynomial to represent the static charge-voltage behavior, and obtained coefficients of determination R2 > 0.99 for the fits. Analysis of the resulting quantitative model reveals an extremely small range of negative differential capacitance <16 mV. The hysteresis loops in the dynamical charge-voltage curves are found to result primarily from energy loss during the ferroelectric transitions, as represented by a frequency-dependent series resistance in the model.
{"title":"Accurate compact nonlinear dynamical model for a volatile ferroelectric ZrO2 capacitor","authors":"Shiva Asapu, Taehwan Moon, Krishnamurthy Mahalingam, Kurt G. Eyink, James Nicolas Pagaduan, Ruoyu Zhao, Sabyasachi Ganguli, Reika Katsumata, Qiangfei Xia, R. Stanley Williams, J. Joshua Yang","doi":"10.1038/s44335-024-00007-z","DOIUrl":"10.1038/s44335-024-00007-z","url":null,"abstract":"We have measured the dynamical response of ZrO2 capacitors to applied triangular voltage waveforms with varying frequencies and amplitudes to determine the voltage and charge on the devices as a function of time. We have fit our experimental results to a Landau–Khalatnikov dynamical equation with a sixth order Landau–Ginzburg–Devonshire polynomial to represent the static charge-voltage behavior, and obtained coefficients of determination R2 > 0.99 for the fits. Analysis of the resulting quantitative model reveals an extremely small range of negative differential capacitance <16 mV. The hysteresis loops in the dynamical charge-voltage curves are found to result primarily from energy loss during the ferroelectric transitions, as represented by a frequency-dependent series resistance in the model.","PeriodicalId":501715,"journal":{"name":"npj Unconventional Computing","volume":" ","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44335-024-00007-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142117950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-21DOI: 10.1038/s44335-024-00006-0
Yifei Yu, Shaocong Wang, Meng Xu, Woyu Zhang, Bo Wang, Jichang Yang, Songqi Wang, Yue Zhang, Xiaoshan Wu, Hegan Chen, Dingchen Wang, Xi Chen, Ning Lin, Xiaojuan Qi, Dashan Shang, Zhongrui Wang
The broad integration of 3D sensors into devices like smartphones and AR/VR headsets has led to a surge in 3D data, with point clouds becoming a mainstream representation method. Efficient real-time learning of point cloud data on edge devices is crucial for applications such as autonomous vehicles and embodied AI. Traditional machine learning models on digital processors face limitations, with software challenges like high training complexity, and hardware challenges such as large time and energy overheads due to von Neumann bottleneck. To address this, we propose a software-hardware co-designed random memristor-based dynamic graph CNN (RDGCNN). Software-wise, we transform point cloud into graph, and propose random EdgeConv for efficient hierarchical and topological features extraction. Hardware-wise, leveraging memristor’s intrinsic stochasticity and in-memory computing capability, we achieve significant reductions in training complexity and energy consumption. RDGCNN demonstrates high accuracy and efficiency across various point cloud tasks, paving the way for future edge 3D vision.
三维传感器广泛集成到智能手机和 AR/VR 头显等设备中，导致三维数据激增，点云成为主流表示方法。在边缘设备上对点云数据进行高效的实时学习对于自动驾驶汽车和嵌入式人工智能等应用至关重要。数字处理器上的传统机器学习模型面临诸多限制，软件方面的挑战包括训练复杂度高，硬件方面的挑战包括冯-诺依曼瓶颈导致的大量时间和能源开销。为此，我们提出了一种软硬件协同设计的基于随机忆阻器的动态图 CNN（RDGCN）。在软件方面，我们将点云转换为图，并提出了随机 EdgeConv 以实现高效的层次和拓扑特征提取。在硬件方面，我们利用忆阻器固有的随机性和内存计算能力，显著降低了训练复杂度和能耗。RDGCNN 在各种点云任务中都表现出了高精度和高效率，为未来的边缘 3D 视觉铺平了道路。
{"title":"Random memristor-based dynamic graph CNN for efficient point cloud learning at the edge","authors":"Yifei Yu, Shaocong Wang, Meng Xu, Woyu Zhang, Bo Wang, Jichang Yang, Songqi Wang, Yue Zhang, Xiaoshan Wu, Hegan Chen, Dingchen Wang, Xi Chen, Ning Lin, Xiaojuan Qi, Dashan Shang, Zhongrui Wang","doi":"10.1038/s44335-024-00006-0","DOIUrl":"10.1038/s44335-024-00006-0","url":null,"abstract":"The broad integration of 3D sensors into devices like smartphones and AR/VR headsets has led to a surge in 3D data, with point clouds becoming a mainstream representation method. Efficient real-time learning of point cloud data on edge devices is crucial for applications such as autonomous vehicles and embodied AI. Traditional machine learning models on digital processors face limitations, with software challenges like high training complexity, and hardware challenges such as large time and energy overheads due to von Neumann bottleneck. To address this, we propose a software-hardware co-designed random memristor-based dynamic graph CNN (RDGCNN). Software-wise, we transform point cloud into graph, and propose random EdgeConv for efficient hierarchical and topological features extraction. Hardware-wise, leveraging memristor’s intrinsic stochasticity and in-memory computing capability, we achieve significant reductions in training complexity and energy consumption. RDGCNN demonstrates high accuracy and efficiency across various point cloud tasks, paving the way for future edge 3D vision.","PeriodicalId":501715,"journal":{"name":"npj Unconventional Computing","volume":" ","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44335-024-00006-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142013667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-05DOI: 10.1038/s44335-024-00005-1
Fiona Knoll, John Daly, Jess Meyer
Decades of exponential scaling in high-performance computing (HPC) efficiency is coming to an end. Transistor-based logic in complementary metal-oxide semiconductor (CMOS) technology is approaching physical limits beyond which further miniaturization will be impossible. Future HPC efficiency gains will necessarily rely on new technologies and paradigms of computing. The Ising model shows particular promise as a future framework for highly energy-efficient computation. Ising systems are able to operate at energies approaching thermodynamic limits for energy consumption of computation. Ising systems can function as both logic and memory. Thus, they have the potential to significantly reduce energy costs inherent to CMOS computing by eliminating costly data movement. The challenge in creating Ising-based hardware is in optimizing useful circuits that produce correct results on fundamentally nondeterministic hardware. The contribution of this paper is a novel machine learning approach, a combination of deep neural networks and random forests, for efficiently solving optimization problems that minimize sources of error in the Ising model. In addition, we provide a process to express a Boltzmann probability optimization problem as a supervised machine learning problem.
{"title":"Solving Boltzmann optimization problems with deep learning","authors":"Fiona Knoll, John Daly, Jess Meyer","doi":"10.1038/s44335-024-00005-1","DOIUrl":"10.1038/s44335-024-00005-1","url":null,"abstract":"Decades of exponential scaling in high-performance computing (HPC) efficiency is coming to an end. Transistor-based logic in complementary metal-oxide semiconductor (CMOS) technology is approaching physical limits beyond which further miniaturization will be impossible. Future HPC efficiency gains will necessarily rely on new technologies and paradigms of computing. The Ising model shows particular promise as a future framework for highly energy-efficient computation. Ising systems are able to operate at energies approaching thermodynamic limits for energy consumption of computation. Ising systems can function as both logic and memory. Thus, they have the potential to significantly reduce energy costs inherent to CMOS computing by eliminating costly data movement. The challenge in creating Ising-based hardware is in optimizing useful circuits that produce correct results on fundamentally nondeterministic hardware. The contribution of this paper is a novel machine learning approach, a combination of deep neural networks and random forests, for efficiently solving optimization problems that minimize sources of error in the Ising model. In addition, we provide a process to express a Boltzmann probability optimization problem as a supervised machine learning problem.","PeriodicalId":501715,"journal":{"name":"npj Unconventional Computing","volume":" ","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44335-024-00005-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141968493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-25DOI: 10.1038/s44335-024-00003-3
Yang Lv, Brandon R. Zink, Robert P. Bloom, Hüsrev Cılasun, Pravin Khanal, Salonik Resch, Zamshed Chowdhury, Ali Habiboglu, Weigang Wang, Sachin S. Sapatnekar, Ulya Karpuzcu, Jian-Ping Wang
The conventional computing paradigm struggles to fulfill the rapidly growing demands from emerging applications, especially those for machine intelligence because much of the power and energy is consumed by constant data transfers between logic and memory modules. A new paradigm, called “computational random-access memory (CRAM),” has emerged to address this fundamental limitation. CRAM performs logic operations directly using the memory cells themselves, without having the data ever leave the memory. The energy and performance benefits of CRAM for both conventional and emerging applications have been well established by prior numerical studies. However, there is a lack of experimental demonstration and study of CRAM to evaluate its computational accuracy, which is a realistic and application-critical metric for its technological feasibility and competitiveness. In this work, a CRAM array based on magnetic tunnel junctions (MTJs) is experimentally demonstrated. First, basic memory operations, as well as 2-, 3-, and 5-input logic operations, are studied. Then, a 1-bit full adder with two different designs is demonstrated. Based on the experimental results, a suite of models has been developed to characterize the accuracy of CRAM computation. Scalar addition, multiplication, and matrix multiplication, which are essential building blocks for many conventional and machine intelligence applications, are evaluated and show promising accuracy performance. With the confirmation of MTJ-based CRAM’s accuracy, there is a strong case that this technology will have a significant impact on power- and energy-demanding applications of machine intelligence.
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