Zhihua Xiao

Machine learning algorithms have proven to be effective for essential quantum computation tasks such as quantum error correction and quantum control. Efficient hardware implementation of these algorithms at cryogenic temperatures is essential. Here we utilize magnetic topological insulators as memristors (termed magnetic topological memristors) and introduce a cryogenic in-memory computing scheme based on the coexistence of a chiral edge state and a topological surface state. The memristive switching and reading of the giant anomalous Hall effect exhibit high energy efficiency, high stability and low stochasticity. We achieve high accuracy in a proof-of-concept classification task using four magnetic topological memristors. Furthermore, our algorithm-level and circuit-level simulations of large-scale neural networks demonstrate software-level accuracy and lower energy consumption for image recognition and quantum state preparation compared with existing magnetic memristor and complementary metal-oxide-semiconductor technologies. Our results not only showcase a new application of chiral edge states but also may inspire further topological quantum-physics-based novel computing schemes.

Cryogenic in-memory computing, operating at temperatures below 4.2 K, offers high performance and energy efficiency in computation-intensive environments, especially for quantum controls. Spintronic memristors based on anomalous Hall effect (AHE) can be utilized with advantages of multi-bit bipolar weights and nearly unlimited endurance. However, there exists two major challenges in realizing an AHE-based neural network (NN): first, anomalous Hall resistances RH typically range in the order of a few ohms in conventional ferromagnets; second, signal-to-noise ratio of summation and read disturbance can lead to errors in NN operation. In this work, we demonstrate large RH of 12 kΩ at 2 K using a magnetic topological insulator (MTI) utilizing the quantum AHE, and for the first time, propose and experimentally verify the multiply-and-accumulate operation of a transverse-read Hall-current based neural network (Hall NN). Simulation of the proposed MTI Hall NN achieves matching accuracy to a full-precision network in the noisy qubit state preparation task. Compared with the MRAM NN, the MTI NN features a 90% lower write energy and 10 times higher TOPS/W showing the promise of MTI Hall NNs.

The probabilistic bit (P-bit) is the core of probabilistic computing. We propose a novel in-situ P-bit compatible with compute-in-memory (CIM) schemes using voltage-controlled magnetic tunnel junctions (MTJs) to eliminate the generation-sample-transfer-compute paradigm of current P-bits. The conventional approach of sampling and transferring random sequences between separate P-bits and computing units reintroduces the memory bottleneck seen in von Neumann architectures, thereby limiting the efficiency of probabilistic computing. By pairing a data-bit and a P-bit as a probabilistic differential pair in a crossbar array, we enable random sequences to be directly utilized for computing. This generationcompute scheme eradicates the sampling and transfer costs associated with previous probabilistic computing methods. Full reuse of devices in the differential cells allows for probabilistic CIM with a large number of P-bits and high parallelism, suitable for real-world probabilistic computing tasks. We demonstrated in-memory neural stochastic differential equations for the reverse diffusion process in generative models. The results shows that without the bottlenecks, in-situ P-bit throughput is 6× faster and 2.19× more efficient than ex-situ P-bits using the same technology. Compared to other devices and schemes, the proposed scheme is 3× faster than state-of-the-art CMOS designs and 1.36× more energy efficient.

Memristors have emerged as promising devices for enabling efficient multiply-accumulate (MAC) operations in crossbar arrays, crucial for analog in-memory computing (AiMC). However, variations in memristors and associated circuits can affect the accuracy of analog computing. Typically, this is mitigated by on-chip training, which is challenging for memristors with limited endurance. We present a hardware-software codesign using magnetic tunnel junction (MTJ)–based AiMC off-chip calibration that achieves software accuracy without costly on-chip training. Hardware-wise, MTJ devices exhibit ultralow cycle-to-cycle variations, as experimentally evaluated over 1 million mass-produced devices. Software-wise, leveraging this, we propose an off-chip training method to adjust deep neural network parameters, achieving accurate AiMC inference. We validate this approach with MAC operations, showing improved transfer curve linearity and reduced errors. By emulating large-scale neural network models, our codesigned MTJ-based AiMC closely matches software baseline accuracy and outperforms existing off-chip training methods, highlighting MTJ’s potential in AI tasks.

In the pursuit of advancing neuromorphic computing, our research presents a novel method for generating and precisely controlling intermediate states within heavy metal/ferromagnet systems. These states are engineered through the interplay of a strong in-plane magnetic field and an applied charge current. We provide a method for fine-tuning these states by introducing a small out-of-plane magnetic field, allowing for the modulation of the system’s probabilistic response to varying current levels. We also demonstrate the implementation of a spiking neural network (SNN) with a tri-state spike timing-dependent plasticity (STDP) learning rule using our devices. Our research furthers the development of spintronics and informs neural system design. These intermediate states can serve as synaptic weights or neuronal activations, paving the way for multi-level neuromorphic computing architectures.

Hardware-accelerated artificial intelligence with emerging nonvolatile memory such as spin-transfer torque-magneto-resistive random-access memory (STT-MRAM) is pushing both the algorithm and hardware to their design limits. The restrictions for analog-based in-memory computing (IMC) include the device variation, IR drop effect due to low resistance of STT-MRAM and read disturbance in the memory array at the advanced technology node. On-chip hybrid training can recover the inference accuracy but at the cost of many training epochs, reducing the available lifetime for updating cycles needed for on-chip inference. In this work, we show the unique feature of device variations in the foundry STT-MRAM array and propose a software-hardware cross-layer co-design scheme for STT-MRAM IMC. By sensing device level variations, we can leverage them for more conductance levels to adaptively quantize the deep neural networks (DNNs). This device variation-aware adaptive quantization (DVAQ) scheme enables a DNN inference accuracy comparable to on-chip hybrid training without on-chip training. Besides, this DVAQ scheme greatly reduces IR drop effects. Overall, the DVAQ allows one to achieve less than a 1% accuracy drop compared with in-situ training under 40 % device variation/noise without on-chip training in several DNN applications.

Energy-efficient hardware implementation of machine learning algorithms for quantum computation requires nonvolatile and electrically-programmable devices, memristors, working at cryogenic temperatures that enable in-memory computing. Magnetic topological insulators are promising candidates due to their tunable magnetic order by electrical currents with high energy efficiency. Here, we utilize magnetic topological insulators as memristors (termed magnetic topological memristors) and introduce a chiral edge state-based cryogenic in-memory computing scheme. On the one hand, the chiral edge state can be tuned from left-handed to right-handed chirality through spin-momentum locked topological surface current injection. On the other hand, the chiral edge state exhibits giant and bipolar anomalous Hall resistance, which facilitates the electrical readout. The memristive switching and reading of the chiral edge state exhibit high energy efficiency, high stability, and low stochasticity. We achieve high accuracy in a proof-of-concept classification task using four magnetic topological memristors. Furthermore, our algorithm-level and circuit-level simulations of large-scale neural networks based on magnetic topological memristors demonstrate a software-level accuracy and lower energy consumption for image recognition and quantum state preparation compared with existing memristor technologies. Our results may inspire further topological quantum physics-based novel computing schemes.

Binary neural network (BNN) has shown great potential to be implemented with power efficiency and high throughput. Compared with its counterpart, the convolutional neural network (CNN), BNN is trained with binary constrained weights and activations, which are more suitable for edge devices with less computing and storage resource requirements. In this paper, we introduce the BNN characteristics, basic operations and the binarized-network optimization methods. Then we summarize several accelerator designs for BNN hardware implementation by using three mainstream structures, i.e., ReRAM-based crossbar, FPGA and ASIC. Based on the BNN characteristics and hardware custom designs, all these methods achieve massively parallelized computations and highly pipelined data flow to enhance its latency and throughput performance. In addition, the intermediate data with the binary format are stored and processed on chip by constructing the computing-in-memory (CIM) architecture to reduce the off-chip communication costs, including power and latency.

A critical mission for mobile robot vision is to detect and classify different objects with low power consumption. In this brief, a multi-class object detection coprocessor is proposed by using the Histogram of Oriented Gradient (HOG) and Local Binary Pattern (LBP) together with a weighted Softmax classifier. Its architecture is compact and hardware-friendly suitable for energy-constrained applications. Firstly, the cell-based feature extraction unit and block-level normalization reuse the SRAMs for storing one-row cell and one-row block. Meanwhile, the working frequency of the feature extraction and block-normalization unit is synchronized to the image sensor for low dynamic power. Then, a block-level one-time sliding-detection-window (OTSDW) mechanism is developed for partial classification with scalable object size. Finally, the Softmax classifier, which is implemented by the look-up table, linear fitting methods, and the fixed-point number, is tested in the Fashion MNIST dataset to evaluate its performance in multi-class classification problems and it reached an accuracy of over 86.2% with 10,180 parameters. The experimental result shows that the hardware-resource usage of the FPGA implementation is capable of 60 fps VGA video with 80.98 mW power consumption. This method uses similar or even fewer hardware resources than that of previous work using only the HOG feature and single-class classifier.