Collaborative Research: FET: Medium: Neuroplane: Scalable Deep Learning through Gate-tunable MoS2 Crossbars

合作研究：FET：媒介：神经平面：通过门可调 MoS2 交叉开关进行可扩展深度学习

• 批准号: 2106964
• 负责人: Mark Hersam
• 金额: $ 50万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2106964&HistoricalAwards=false
• 关键词: Collaborative; Research; FET; Medium; Neuroplane

The increasing complexity of deep-learning systems has pushed conventional computing technologies to their limits. While the memristor is one of the prevailing technologies for deep-learning acceleration, it is only suited for classical learning layers where two operands, namely weights and inputs, are processed at a time. Meanwhile, to improve the computational efficiency of deep learning for emerging applications, a variety of non-traditional layers, requiring concurrent higher-order processing of many operands, are becoming popular. For example, hypernetworks improve their predictive robustness by simultaneously processing weights and inputs against the application context. Two-electrode memristor grids cannot natively support such operations of emerging layers. Addressing the unmet need, this research will develop Neuroplane -- a novel deep-learning accelerator of gated memtransistor crossbars. Exploiting crossbars' gate controllability, multiple operands can be processed within the same crossbar unit in Neuroplane. Many advanced inference architectures that can generalize beyond a typical passive crossbar will thus be possible. Overall, the ultra-low-power, higher-order processing of Neuroplane will harness high robustness and efficiency of emerging deep-learning layers within area/power-constrained devices such as mobile, sensor, and embedded systems.The investigators will develop fabrication methods for nanometer node gate-tunable dual-gated crossbars of MoS2 memtransistors. A self-aligned fabrication method with defect passivation and process variability compensation will be created. Exploiting the gate-tunability of MoS2 memtransistors, a new generation of crossbar platforms with many runtime control knobs will be developed, rendering the design a high elasticity and agile computing space. For example, computing methods will be created for the gated crossbars to utilize crossbar elements for product-sum digitization, thereby preventing the critical overheads in current crossbar technologies. Similarly, control-flow methods will be developed for gated crossbars to adapt their inference paths depending on the input characteristics by dynamically deactivating input/output neurons to conserve processing energy. A coherent collection of software and hardware-based correction techniques is proposed to minimize the impact of process variability. Unlike the current schemes, by following the train-once-deploy-anywhere tenet, the proposed crossbar correction methods can scale to millions of deployments without considerable overhead. An annual workshop will be conducted at local high schools with substantial ethnic and gender diversity to mentor underrepresented students. Undergraduate research projects will be sponsored using paid summer internships and university-level programs such as summer undergraduate fellowship. An inter-university senior-design mentoring program will be created for students among participating institutions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

深度学习系统日益复杂，已将传统计算技术推向极限。虽然忆阻器是深度学习加速的流行技术之一，但它仅适用于一次处理两个操作数（即权重和输入）的经典学习层。与此同时，为了提高新兴应用深度学习的计算效率，需要对许多操作数进行并发高阶处理的各种非传统层正在变得流行。例如，超网络通过同时处理应用程序上下文的权重和输入来提高其预测鲁棒性。二电极忆阻器网格本身无法支持新兴层的此类操作。为了解决未满足的需求，这项研究将开发 Neuroplane——一种新颖的门控记忆晶体管交叉开关深度学习加速器。利用交叉开关的门可控性，可以在 Neuroplane 中的同一交叉开关单元内处理多个操作数。因此，许多可以超越典型无源交叉开关的高级推理架构将成为可能。总体而言，Neuroplane 的超低功耗、高阶处理将利用区域/功耗受限设备（例如移动设备、传感器和嵌入式系统）中新兴深度学习层的高鲁棒性和效率。研究人员将开发制造方法用于 MoS2 记忆晶体管的纳米节点栅极可调双栅极交叉开关。将创建具有缺陷钝化和工艺变异补偿的自对准制造方法。利用MoS2记忆晶体管的栅极可调性，将开发具有许多运行时控制旋钮的新一代交叉开关平台，从而使设计具有高弹性和敏捷的计算空间。例如，将为门控交叉开关创建计算方法，以利用交叉开关元件进行乘积和数字化，从而防止当前交叉开关技术中的关键开销。类似地，将为门控交叉开关开发控制流方法，通过动态停用输入/输出神经元来根据输入特征调整其推理路径，以节省处理能量。提出了基于软件和硬件的校正技术的一致集合，以最大限度地减少过程可变性的影响。与当前方案不同，通过遵循一次训练、随处部署的原则，所提出的交叉校正方法可以扩展到数百万次部署，而无需相当大的开销。年度研讨会将在种族和性别多元化的当地高中举办，以指导代表性不足的学生。本科生研究项目将通过带薪暑期实习和大学水平项目（例如暑期本科生奖学金）来资助。将为参与机构中的学生创建一个大学间高级设计指导计划。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Higher order neural processing with input-adaptive dynamic weights on MoS2 memtransistor crossbars

• DOI: 10.3389/femat.2022.950487
• 发表时间: 2022-08
• 期刊: Scandinavian Actuarial Journal
• 影响因子: 1.8
• 作者: Leila Rahimifard;Ahish Shylendra -Ahish-Shylendra -2180672644;Shamma Nasrin -Shamma-Nasrin -2180672195;Stephanie E. Liu -Stephanie-E.-Liu -2180672856;Vinod K. Sangwan -Vinod-K.-Sangwan -2180672356;Mark C. Hersam -Mark-C.-Hersam -2180672622;A. Trivedi

Two-dimensional materials for bio-realistic neuronal computing networks

• DOI: 10.1016/j.matt.2022.10.017
• 发表时间: 2022-12
• 期刊: Matter
• 影响因子: 18.9
• 作者: V. Sangwan;Stephanie E. Liu;A. Trivedi;M. Hersam