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

深度学习系统的日益复杂已经将传统计算技术推向了极限。虽然忆阻器是深度学习加速的主流技术之一，但它只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，为了提高新兴应用的深度学习的计算效率，各种需要并行高阶处理许多操作数的非传统层变得流行起来。例如，超网络通过同时处理针对应用程序上下文的权重和输入来提高其预测稳健性。双电极记忆阻栅不能自然地支持新兴层的这种操作。针对这一未得到满足的需求，这项研究将开发一种新型的门控记忆晶体管纵横杆深度学习加速器--NeuroPlane。利用Crosbar的栅极可控性，在NeuroPlane的同一个Crosbar单元中可以处理多个操作数。因此，许多可以超越典型无源交叉开关的高级推理体系结构将成为可能。总体而言，NeuroPlane的超低功耗、高阶处理将利用移动、传感器和嵌入式系统等面积/功率受限设备中新兴的深度学习层的高健壮性和高效率。研究人员将开发纳米节点栅极可调的MoS2记忆晶体管双栅交叉棒的制造方法。将创建一种具有缺陷钝化和工艺可变性补偿的自对准制造方法。利用MoS2记忆晶体管的栅极可调谐性，将开发具有许多运行时控制旋钮的新一代交叉开关平台，使设计具有高度弹性和灵活的计算空间。例如，将为门控纵横杆创建计算方法，以利用纵横杆元件进行积和数字化，从而防止当前纵横杆技术中的关键开销。类似地，控制流方法将被开发用于门控纵横杆，通过动态去激活输入/输出神经元来调整其推理路径，以节省处理能量。提出了基于软件和硬件的校正技术的连贯集合，以最大限度地减少工艺可变性的影响。与目前的方案不同，通过遵循训练一次部署在任何地方的原则，所提出的纵横制校正方法可以扩展到数百万次部署而不需要相当大的开销。一年一度的讲习班将在种族和性别差异很大的当地高中举行，以指导代表人数不足的学生。本科生研究项目将通过带薪暑期实习和大学层面的项目(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跨大学高级设计指导计划。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.