Resistive Memory-based Neural Differential Equation Solver for Score-based Diffusion Model

Human brains image complicated scenes when reading a novel. Replicating this imagination is one of the ultimate goals of AI-Generated Content (AIGC). However, current AIGC methods, such as score-based diffusion, are still deficient in terms of rapidity and efficiency. This deficiency is rooted in the difference between the brain and digital computers. Digital computers have physically separated storage and processing units, resulting in frequent data transfers during iterative calculations, incurring large time and energy overheads. This issue is further intensified by the conversion of inherently continuous and analog generation dynamics, which can be formulated by neural differential equations, into discrete and digital operations. Inspired by the brain, we propose a time-continuous and analog in-memory neural differential equation solver for score-based diffusion, employing emerging resistive memory. The integration of storage and computation within resistive memory synapses surmount the von Neumann bottleneck, benefiting the generative speed and energy efficiency. The closed-loop feedback integrator is time-continuous, analog, and compact, physically implementing an infinite-depth neural network. Moreover, the software-hardware co-design is intrinsically robust to analog noise. We experimentally validate our solution with 180 nm resistive memory in-memory computing macros. Demonstrating equivalent generative quality to the software baseline, our system achieved remarkable enhancements in generative speed for both unconditional and conditional generation tasks, by factors of 64.8 and 156.5, respectively. Moreover, it accomplished reductions in energy consumption by factors of 5.2 and 4.1. Our approach heralds a new horizon for hardware solutions in edge computing for generative AI applications

Random resistive memory-based deep extreme point learning machine for unified visual processing

Visual sensors, including 3D LiDAR, neuromorphic DVS sensors, and conventional frame cameras, are increasingly integrated into edge-side intelligent machines. Realizing intensive multi-sensory data analysis directly on edge intelligent machines is crucial for numerous emerging edge applications, such as augmented and virtual reality and unmanned aerial vehicles, which necessitates unified data representation, unprecedented hardware energy efficiency and rapid model training. However, multi-sensory data are intrinsically heterogeneous, causing significant complexity in the system development for edge-side intelligent machines. In addition, the performance of conventional digital hardware is limited by the physically separated processing and memory units, known as the von Neumann bottleneck, and the physical limit of transistor scaling, which contributes to the slowdown of Moore's law. These limitations are further intensified by the tedious training of models with ever-increasing sizes. We propose a novel hardware-software co-design, random resistive memory-based deep extreme point learning machine (DEPLM), that offers efficient unified point set analysis. We show the system's versatility across various data modalities and two different learning tasks. Compared to a conventional digital hardware-based system, our co-design system achieves huge energy efficiency improvements and training cost reduction when compared to conventional systems. Our random resistive memory-based deep extreme point learning machine may pave the way for energy-efficient and training-friendly edge AI across various data modalities and tasks

Advances in using PARP inhibitors to treat cancer

The poly (ADP-ribose) polymerase (PARP) family of enzymes plays a critical role in the maintenance of DNA integrity as part of the base excision pathway of DNA repair. PARP1 is overexpressed in a variety of cancers, and its expression has been associated with overall prognosis in cancer, especially breast cancer. A series of new therapeutic agents that are potent inhibitors of the PARP1 and PARP2 isoforms have demonstrated important clinical activity in patients with breast or ovarian cancers that are caused by mutations in either the BRCA1 or 2 genes. Results from such studies may define a new therapeutic paradigm, wherein simultaneous loss of the capacity to repair DNA damage may have antitumor activity in itself, as well as enhance the antineoplastic potential of cytotoxic chemotherapeutic agents

Emergence of rationally designed therapeutic strategies for breast cancer targeting DNA repair mechanisms

Accumulating evidence suggests that many cancers, including BRCA1- and BRCA2-associated breast cancers, are deficient in DNA repair processes. Both hereditary and sporadic breast cancers have been found to have significant downregulation of repair factors. This has provided opportunities to exploit DNA repair deficiencies, whether acquired or inherited. Here, we review efforts to exploit DNA repair deficiencies in tumors, with a focus on breast cancer. A variety of agents, including PARP (poly [ADP-ribose] polymerase) inhibitors, are currently under investigation in clinical trials and available results will be reviewed

Prenatal whole exome sequencing identified two rare compound heterozygous variants in EVC2 causing Ellis‐van Creveld syndrome

Abstract Background Pathogenic mutations in EVC or EVC2 gene can lead to Ellis‐van Creveld (EvC) syndrome, which is a rare autosomal recessive skeletal dysplasia disorder. This study aimed to determine pathogenic gene variations associated with EvC syndrome in fetuses showing ultrasound anomalies. Methods A 32‐year‐old pregnant woman from Quanzhou, China was investigated. In her pregnancy examination, the fetus exhibited multiple fetal malformations, including a narrow thorax, short limbs, postaxial polydactyly, cardiac malformations, and separation of double renal pelvis. Karyotype, chromosomal microarray analysis and whole exome sequencing were performed for prenatal genetic etiology analysis. Results Chromosome abnormalities and copy number variants were not observed in the fetus using karyotype and chromosomal microarray analysis. Using whole exome sequencing, two compound heterozygous variants NM_147127.5:c.[2484G>A(p.Trp828Ter)];[871‐2_894del] in EVC2 gene were identified in the fetus as pathogenic variants inherited from parents. Conclusions The study is the first to identify two rare compound variants in EVC2 gene in a Chinese family using whole exome sequencing. The application of whole‐exome sequencing would be helpful in fetal etiological diagnosis with ultrasound anomalies

Low-complexity Reinforcement Learning Decoders for Autonomous, Scalable, Neuromorphic intra-cortical Brain Machine Interfaces

General Description. This dataset consists of data from four BMI experiments performed on two adult macaques. Three of the experiments were joystick controlled tasks, and one of them was center-out reaching task. The macaques were able to use a wireless integrated system to control a robotic platform, over which they were sitting, to achieve independent mobility using the neuronal activity in their motor cortices. The activity of populations of single neurons was recorded using multiple electrode arrays implanted in the arm region of primary motor cortex. A general description is provided below: A titanium head post (Crist Instruments, MD, USA) was affixed prior to implantation of microelectrode arrays in both NHPs. In NHP-A, 4 microelectrode arrays containing 16 electrodes each, and in NHP-B, 1 microelectrode array containing 100 electrodes were implanted in the hand/arm region of the left primary motor cortex respectively. Spike signals were acquired using an in-house 100-channel wireless neural recording system, which is sampled at 13 KHz. The wide-band signals were then band-pass filtered between 300 to 3000 Hz to remove low-frequency components. The threshold for spike deterction was found using the formula: (Thr = 5σ; σ = median(|x|/0.6745), where x is the filtered signal, and σ is an estimate of the standard deviation of the background noise. The behavorial task was to make a robotic wheelchair bound control its motion through a three-directional spring-loaded joystick (Experiment 1, 2, and 3). The experiment comprised of four tasks - a) turning 90° right, b) moving forward by 2m, c) turning 90° left, and d) staying still for 5 seconds (stop task). Successful task completion varied from experiment to experiment. Experiment 4 also involved joystick control but the primate was trained to perform classical center-out task. Data for Experiment 1 and 3 are already publicly available at: https://osf.io/dce96/. However, a detailed description is also provided here. The data are grouped in form of folders containing data for NHP-1/2-Set 1/2. For the folder, NHP 1 Set 1, experiment 1 data consists of sessions 1,2,3; expt 3: 5,6,7,8. Similarly for Set 2: expt-1: 3,4,5,10,11; expt 3: 8,9. For the folder, NHP 2 Set 1, expt 1 consists of sessions 10,11,12,13,18,19,20,21; expt 3: 15,16,17,24. For the folder. Similarly for Set 2: expt-1: 1,2,3,10,11,12,13; expt 3: 6,7,8,9. Possible use cases. These data are ideal for designing, training, and testing iBMI decoders. We expect that the dataset will be valuable for researchers who wish to design improved models of sensorimotor cortical spiking or provide an equal footing for comparing different iBMI decoders. We also hope to inspire more work along neuromorphic lines and use of online Reinforcement Learning algorithms for iBMI decoders. Variable names. Each file from Experiment 1 and 3 contains data in the following format. 1. joystick_adfreq: The frequency of operation of the joystick. 2. X_Voltage: The voltage reading corresponding to the x-coordinate (while joystick operation). 3. Y_Voltage: The voltage reading corresponding to the y-coordinate (while joystick operation). 4. Spike_data(Channel Number): The Channel Number corresponding to which the neuronal data is recorded. 5. Spike_data(Cluster): Descripting the cluster on which the channels are placed. 6. Spike_data(Spike Times): The timestamp corresponding to the detection of a spike. 7. Spike_data(Spike Number): The total number of spikes calculated for a particular trial from a particular channel. 8. Spike_data(Mean Spike Waveform): The mean neuronal data (for that trial from a particular channel) describing a spike. 9. Spike_data(Spike Amplitude): The mean spike amplitude of that particular channel. 10. IMETrainingData(SentSignals): The truth labels corresponding to a particular trial. 11. IMETrainingData(Timestamps): Time stamps corresponding to each sent signal (data). 12. IMETrainingData(ReasonFail): String data; Reason if the trial failed. 13. IMETrainingData(TrialOutcomes): Trial results corresponding to successful or unsuccessful! 14. IMETrainingData(StartTime): corresponding to the beginning of each trial. 15. IMETrainingData(EndTime): corresponding to the end of each trial. For files in Experiment 2 and 4, 1. targetTest_Acc: Corresponding direction of the joystick recorded for each trial. (decoded using the decoder) 2. targetTrain: Ground truth label, corresponding to the actual direction of the joystick (for each trial) 3. testingSet_Acc: Number of spike counts from each channel (used for testing corresponding to all the sessions) 4. trainingSet: Number of spike counts from each channel (used for calibration, mostly) Contact Information. We would be delighted to hear from you if you find this dataset valuable, especially if it leads to publication. Corresponding author: A. Ghosh ; A. Basu . Citation. A. Ghosh, S. Shaikh, P. S. V. Sun, C. Libedinsky, R. So, N. Lin, H. Chen, Z. Wang, A. Basu, "Low-complexity Reinforcement Learning Decoders for Autonomous, Scalable, Neuromorphic intra-cortical Brain Machine Interfaces," IEEE Transaction on Neural Networks and Learning Systems (Under review

Spontaneous Threshold Lowering Neuron using Second‐Order Diffusive Memristor for Self‐Adaptive Spatial Attention

Abstract Intrinsic plasticity of neurons, such as spontaneous threshold lowering (STL) to modulate neuronal excitability, is key to spatial attention of biological neural systems. In‐memory computing with emerging memristors is expected to solve the memory bottleneck of the von Neumann architecture commonly used in conventional digital computers and is deemed a promising solution to this bioinspired computing paradigm. Nonetheless, conventional memristors are incapable of implementing the STL plasticity of neurons due to their first‐order dynamics. Here, a second‐order memristor is experimentally demonstrated using yttria‐stabilized zirconia with Ag doping (YSZ:Ag) that exhibits STL functionality. The physical origin of the second‐order dynamics, i.e., the size evolution of Ag nanoclusters, is uncovered through transmission electron microscopy (TEM), which is leveraged to model the STL neuron. STL‐based spatial attention in a spiking convolutional neural network (SCNN) is demonstrated, improving the accuracy of a multiobject detection task from 70% (20%) to 90% (80%) for the object within (outside) the area receiving attention. This second‐order memristor with intrinsic STL dynamics paves the way for future machine intelligence, enabling high‐efficiency, compact footprint, and hardware‐encoded plasticity

Oscillatory Neural Network-Based Ising Machine Using 2D Memristors

Neural networks are increasingly used to solve optimization problems in various fields, including operations research, design automation, and gene sequencing. However, these networks face challenges due to the nondeterministic polynomial time (NP)-hard issue, which results in exponentially increasing computational complexity as the problem size grows. Conventional digital hardware struggles with the von Neumann bottleneck, the slowdown of Moore’s law, and the complexity arising from heterogeneous system design. Two-dimensional (2D) memristors offer a potential solution to these hardware challenges, with their in-memory computing, decent scalability, and rich dynamic behaviors. In this study, we explore the use of nonvolatile 2D memristors to emulate synapses in a discrete-time Hopfield neural network, enabling the network to solve continuous optimization problems, like finding the minimum value of a quadratic polynomial, and tackle combinatorial optimization problems like Max-Cut. Additionally, we coupled volatile memristor-based oscillators with nonvolatile memristor synapses to create an oscillatory neural network-based Ising machine, a continuous-time analog dynamic system capable of solving combinatorial optimization problems including Max-Cut and map coloring through phase synchronization. Our findings demonstrate that 2D memristors have the potential to significantly enhance the efficiency, compactness, and homogeneity of integrated Ising machines, which is useful for future advances in neural networks for optimization problems