Spiking neural network (SNN) has demonstrated its great potential in low-power neuromorphic applications. In SNN, computation activities are associated with the arrival and firing of spikes, its power consumption is directly correlated with the number of spikes propagated in the network. In this paper, we explore two methods to reduce the spike-count in the network, aiming to reduce the power consumption of SNN. We use Poisson distribution function in the input layer and through adjusting the correlation (called the gain in the paper) between the probability of spike generation and input values, the number of spikes in the input layer can be reduced to only 20% of the baseline model with the accuracy degradation of less than 1%. We also exploit the leaky-integrate-and-fire (LIF) mechanism and use the refractory period to reduce the generation of spikes from the neurons in the hidden layers. Through this method, the spike-count is reduced by 20% $$\sim $$ 50% while the performance degradation is still less than 1%. These two spike-count reduction techniques are implemented in Verilog RTL; the power simulation results demonstrate significant power reduction in performing SNN computations. We further discovered that for different network architectures, these two techniques have different trade-offs to achieve optimal spike-count reduction while maintaining satisfactory results. Compared with other spike-count reduction techniques, the proposed scheme is efficient and straightforward for hardware implementation, making it well-suited for edge computing scenarios.

In this work, we present Digital Life Project, a framework utilizing language as the universal medium to build autonomous 3D characters, who are capable of engaging in social interactions and expressing with articulated body motions, thereby simulating life in a digital environment. Our framework comprises two primary components: 1) SocioMind: a meticulously crafted digital brain that models personalities with systematic few-shot exemplars, incorporates a reflection process based on psychology principles, and emulates autonomy by initiating dialogue topics; 2) MoMat-MoGen: a text-driven motion synthesis paradigm for controlling the character's digital body. It integrates motion matching, a proven industry technique to ensure motion quality, with cutting-edge advancements in motion generation for diversity. Extensive experiments demonstrate that each module achieves state-of-the-art performance in its respective domain. Collectively, they enable virtual characters to initiate and sustain dialogues autonomously, while evolving their socio-psychological states. Concurrently, these characters can perform contextually relevant bodily movements. Additionally, an extension of DLP enables a virtual character to recognize and appropriately respond to human players' actions.

We introduce a novel task within the field of human motion generation, termed dance accompaniment, which necessitates the generation of responsive movements from a dance partner, the "follower", synchronized with the lead dancer’s movements and the underlying musical rhythm. Unlike existing solo or group dance generation tasks, a duet dance scenario entails a heightened degree of interaction between the two participants, requiring delicate coordination in both pose and position. To support this task, we first build a large-scale and diverse duet interactive dance dataset, DD100, by recording about 117 minutes of professional dancers’ performances. To address the challenges inherent in this task, we propose a GPT based model, Duolando, which autoregressively predicts the subsequent tokenized motion conditioned on the coordinated information of the music, the leader’s and the follower’s movements. To further enhance the GPT’s capabilities of generating stable results on unseen conditions (music and leader motions), we devise an off-policy reinforcement learning strategy that allows the model to explore viable trajectories from out-of-distribution samplings, guided by human-defined rewards. Based on the collected dataset and proposed method, we establish a benchmark with several carefully designed metrics.

Synthetic data has emerged as a promising source for 3D human research as it offers low-cost access to large-scale human datasets. To advance the diversity and annotation quality of human models, we introduce a new synthetic dataset, SynBody, with three appealing features: 1) a clothed parametric human model that can generate a diverse range of subjects; 2) the layered human representation that naturally offers high-quality 3D annotations to support multiple tasks; 3) a scalable system for producing realistic data to facilitate real-world tasks. The dataset comprises 1.2M images with corresponding accurate 3D annotations, covering 10,000 human body models, 1,187 actions, and various viewpoints. The dataset includes two subsets for human pose and shape estimation as well as human neural rendering. Extensive experiments on SynBody indicate that it substantially enhances both SMPL and SMPL-X estimation. Furthermore, the incorporation of layered annotations offers a valuable training resource for investigating the Human Neural Radiance Fields(NeRF).

Spiking neural networks (SNNs) demonstrate great potentials to achieve low-power computation for AI applications. SNN uses spike trains, instead of binary bit-steams to encode input and output information, therefore, analog implementation of SNN will have more advantages than digital implementation in terms of power consumption and hardware overheads. Leaky Integrate-and-Fire (LIF) and Spike Timing Dependent Plasticity (STDP) models are the two fundamental mechanisms of SNN operation. In this paper, we propose a 55nm analog CMOS implementation of the LIF and STDP functions. Testing results demonstrate that the circuit can closely imitate the behavior of the LIF and STDP mechanisms, while demanding a much lower power consumption (around 1nJ per spike with the pulse width of 0.5ms). The proposed LIF and STDP circuits can be used as building blocks to construct a complete SNN architecture.

Although Artificial Neural Networks (ANNs) are inspired by biological neural systems, most of ANNs today are implemented with digital circuitry and use binary values in computation. In recent years, analog-based neuromorphic system has gained lots of attention as it provides a natural interface for brain-machine interaction. In this paper, we present analog designs of a complete neuron system, where the Multiply-Accumulate (MAC) and Rectified Linear Unit (ReLU) functions are all implemented in analog circuits. The design uses SMIC 55nm standard LP CMOS process node and operates at low supply voltage (1.2 V). The simulation results in SPECTRE demonstrate that the MAC's linear error is no more than 0.5% and total harmonic distortion (THD) is less than 1.6% when the inputs vary from peak (-10 µA) to peak (10 µA) at 10 MHz, the -3dB bandwidth is 288 MHz, the maximum power consumption is 540 µW and the static power consumption is 493 µW under 100MHz input signal frequency. More specifically, our design is resilient to the fluctuation of power supply, which helps to achieve high precision of computation.

Spiking Neural Networks (SNN) is one special implementation of Artificial Neural Networks (ANN), where the input signals are encoded in the temporal relationship between consecutive spikes (spike trains) instead of real-numbered values. Nevertheless, SNN is believed to be a closer representation of the biological neural system, because it imitates the current spikes that are transmitted between neurons in real biological systems. Practical and simplified SNN models include the Leaky Integrate-and-Fire (LIF) function of the neurons and the Spiking Timing Dependent Plasticity (STDP) function of the synapses. While most ANN architectures can be implemented with digital logic gates, SNN is more suitable to be implemented in analog circuits. In this paper, we propose revised analog circuit implementations of SNN neurons with LIF and STDP functions. Compared with previous works by other researchers, our proposed analog designs use fewer components and can be cascaded to form a complete neural system. The circuits are designed and simulated with SMIC 55nm CMOS LP process. The simulated results demonstrate that the analog neural system can work under a very small current (less than 10 µA) and voltage supply (1.0 Volts), and consumes less power consumption than digital implementations.

In cognitive radio (CR) networks, spectrum sensing is an essential operation for unlicensed users to discover available spectrum opportunities for communications. However, current spectrum sensing techniques have a drawback that they can only detect the existence of signals but they cannot differentiate whether the detected signals come from licensed users or unlicensed users. This drawback leads to a significant waste of spectrum resources, which greatly limits the spectrum utilization of unlicensed users. In this paper, we investigate the issue of differentiating the licensed user and unlicensed user signals. A novel spectrum sensing scheduling scheme is proposed to solve this issue. Under the proposed spectrum sensing scheduling scheme, accurate sensing results on licensed users are obtained. Simulation results show that the proposed spectrum sensing scheduling scheme outperforms the scenario without signal differentiation in terms of higher unlicensed user throughput. To the best of our knowledge, this is the first work that investigates the signal differentiation issue between licensed users and unlicensed users in CR networks.