Engineering topics

Pan, Zhenyu

Publications and source records attributed to Pan, Zhenyu.

5 records

DOE OSTI2023

Ising-CF: A Pathbreaking Collaborative Filtering Method Through Efficient Ising Machine Learning

Due to Ising models’ strong expressivity and Ising machines’ unique computational power, it is highly desired if Ising-based learning can be used in real-world applications. Unfortunately, the challenges in learning Ising models and gaps between the practical accuracy of Ising machines and the theoretical accuracy of Ising models impede the realization of Ising machines’ potential. Hence, we propose an Ising Machine Learning framework, Ising-CF, for collaborative filtering, a widely-used recommendation method. Specifically, Ising-CF uses Linear Neural Networks with Besag’s pseudo-likelihood and voltage polarization for fast, accurate Ising model learning and an Ising-specific logarithmic quantization for ns-level Ising machine inference with near-theoretical accuracy, 7.3% over SOTA.

Liu, Zhuo↗

DOE OSTI2023

Software-Hardware Co-design of Heterogeneous SmartNIC System for Recommendation Models Inference and Training

Deep Learning Recommendation Models (DLRMs) are critical applications in various domains and have evolved as one of the single largest machine learning applications. Trillions of DLRM parameters exceed the on-chip memory capacity of GPUs. Large-scale multi-node systems are required for distributed DLRM inference and training, which suffer from the all-to-all communication bottleneck, mainly limiting the scalability of ever-growing DLRMs. In recent years, SmartNICs have evolved with coupled computation and communication capabilities providing opportunities for a powerful heterogeneous device in the system. However, there isn't such a distributed system that fully leverages the abundant smartNIC resources that resolve the scalability issue of DLRMs. In this work, we proposed a software-hardware co-design of a heterogeneous smartNIC system that resolves the communication bottleneck of distributed DLRMs, mitigates the memory bandwidth pressure, and improves computation efficiency. We provide a set of smartNIC designs of cache systems (including local cache and remote cache) and smartNIC computation kernels which reduce data movement, relieve memory lookup intensity, and improve the GPU's computation efficiency. In addition, we propose a graph algorithm that improves the data locality of queries within batches which optimizes the overall system performance with higher data reuse. Our evaluation shows that our system achieves 2.1x latency speedup for inference and 1.6x throughput speedup for training.

Guo, Anqi↗

DOE OSTI2023

Ising-Traffic: Using Ising Machine Learning to Predict Traffic Congestion under Uncertainty

This paper addresses the challenges in accurate and realtime traffic congestion prediction with uncertainty by proposing Ising-Traffic, a novel quantum-inspired dual-model Ising based traffic prediction framework which delivers higher accuracy and lower latency than SOTA solutions. While traditional and deep learning methods face the trade-off between algorithm complexity and computational efficiency, our Ising-based method leverages Ising’s inherent and unique capability of finding the state of a system with the lowest energy and applying it to traffic prediction. In this work, traffic prediction under uncertainty is formulated into two separate Ising models: Reconstruct-Ising and Predict-Ising. Reconstruct-Ising is mapped onto modern Ising machine and handles uncertainty in traffic accurately with negligible latency and energy consumption, while Predict-Ising is mapped onto traditional processors and predicts future congestion precisely with only at most 1.8% computational demands of existing solutions. Our evaluation shows Ising-Traffic delivers on average 98× speedups and 5% accuracy improvement over SOTA.

traffic flow control, Ising↗

DOE OSTI2020

Evaluating the Ratio of Electron and Hole Mobilities from a Single Bulk Sample Using Photo-Seebeck Effect

When a semiconductor is under photoexcitation, the voltage response to a temperature gradient is the photo-Seebeck effect. In this work, we study this effect, focusing on the contribution from transport of photo-excited carriers. We demonstrate that by combining photo-Seebeck with photoconductivity measurements, one can determine the ratio between electron and hole mobilities, and hence both of them when one is known. This is found for the case of defect-free samples, where no detail on the absorbance, carrier lifetime or recombination is necessary. Our method reported here does not require chemical doping, which could introduce defects and is often not feasible. It applies to both thin film and bulk samples. Experiment wise, photo-Seebeck effect is relatively easy to implement, or added to existing systems. In a broader context, for semiconductors with significant influence from defects, our result suggests that the photo-Seebeck behavior can still be understood. In this case another photo-transport property is necessary, in order to identify the mobilities of carriers and information regarding the defects. This framework integrates the information from photoexcitation and thermal gradients to provide a general method to determine fundamental electronic properties of materials.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Solvent-free synthesis of organometallic halides CH 3 NH 3 PbI 3 and (CH 3 NH 3 ) 3 Bi 2 I 9 and their thermoelectric transport properties

Organometallic halides are great candidates for optoelectronics. As an important family of semiconductors, understanding their thermoelectric transport properties is also important. This has been a challenging task as many of such compounds are highly intrinsic. In this work, we synthesized two halides, CH 3 NH 3 PbI 3 and (CH 3 NH 3 ) 3 Bi 2 I 9 , using a solvent-free method. We found an extraordinarily high Seebeck coefficient of +2600 ± 200 μ V/K in (CH 3 NH 3 ) 3 Bi 2 I 9 . For CH 3 NH 3 PbI 3 , our synthesis method led to a negative Seebeck coefficient of –1350 ± 50 μ V/K, in contrast to positive values observed in solvent synthesized samples. We also found the thermal conductivity of CH 3 NH 3 PbI 3 to be 0.38 W/m K, largely independent of temperature from 300 K to 450 K, despite a tetragonal-to-cubic phase transition. (CH 3 NH 3 ) 3 Bi 2 I 9 has an even lower thermal conductivity of 0.21 W/m K (also temperature independent) which is due to its soft phonon dispersion and weak bonds.

36 MATERIALS SCIENCE↗