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的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，但它只适用于同时处理两个操作数（即权重和输入）的经典学习层。与此同时，为了提高新兴应用中深度学习的计算效率，需要对多个操作数进行并发高阶处理的各种非传统层正变得流行起来。例如，超级网络通过针对应用程序上下文同时处理权重和输入来提高其预测健壮性。双电极忆阻栅格本身不能支持这种新兴层的操作。为了解决未满足的需求，本研究将开发Neuroplane——一种新型的门控memtransistor crossbars深度学习加速器。利用横杆的栅极可控性，可以在同一横杆单元内处理多个操作数。因此，许多可以泛化到典型的无源交叉杆之外的高级推理体系结构将成为可能。总的来说，Neuroplane的超低功耗、高阶处理将在移动、传感器和嵌入式系统等面积/功率受限的设备中利用新兴深度学习层的高鲁棒性和高效率。研究人员将开发二硫化钼mem晶体管的纳米节点栅可调谐双门交叉栅的制造方法。提出了一种缺陷钝化和工艺变异性补偿的自对准制造方法。利用MoS2 mem晶体管的栅极可调性，将开发具有多个运行时控制旋钮的新一代交叉杆平台，为设计提供高弹性和敏捷的计算空间。例如，将为门控横杆创建计算方法，以利用横杆元素进行积和数字化，从而防止当前横杆技术中的关键开销。类似地，将为门控交叉杆开发控制流方法，通过动态停用输入/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一致集合，以尽量减少过程可变性的影响。与当前方案不同的是，通过遵循训练-一次部署-任何地方的原则，所提出的交叉栏校正方法可以扩展到数百万个部署，而不会产生相当大的开销。将在种族和性别相当多样化的当地高中举办年度讲习班，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级别的项目（如暑期本科生奖学金）来资助。将为参与院校之间的学生创建大学间高级设计指导计划。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.