Graph attention network can generate effective feature embedding by specifying different weights to different nodes. The key of the research on heterogeneous graph embedding is the way to combine its rich structural information with semantic relations to aggregate the neighborhood information. Most of the existing heterogeneous graph representation learning methods guide the selection of neighbors by defining various meta-paths on heterogeneous graphs. However, these models only consider the information contained in the nodes under different paths and ignore the potential semantic relationships of nodes in different neighbor graph structures, which leads to the underutilization of graph structure information. In this article, we propose a novel adaptive framework named Neighbor Graph Aggregated Graph Attention Network (NGGAN) to fully exploit graph topological details in heterogeneous graph, and aggregates their information to obtain an effective embedding. The key idea is to use different levels of sampling methods to define neighborhood, and use neighbor graphs to represent the complex structural interaction between nodes. In this way, the high-order relationship between nodes and the latent semantics of neighbor graphs can be fully explored. Afterward, a hierarchical attention mechanism is applied to adaptively learn the importance of different objects, including node information, path information, and neighbor graph information. Multiple downstream tasks are performed on four real-world heterogeneous graph datasets, and the experimental results demonstrate the effectiveness of NGGAN.

In this work, we propose the first bit-level sparsity-aware SAR ADC with direct hybrid encoding for signed expressions (HESE) for AIoT applications. ADCs are typically a bottleneck in reducing the energy consumption of analog neural networks (ANNs). For a pre-trained Convolutional Neural Network (CNN) inference, a HESE SAR for an ANN can reduce the number of non-zero signed digit terms to be output, and thus enables a reduction in energy along with the term quantization (TQ). The proposed SAR ADC directly produces the HESE signed-digit representation (SDR) using two thresholds per cycle for 2-bit look-ahead (LA). A prototype in 65nm shows that the HESE SAR provides sparsity encoding with a Walden FoM of 15.2fJ/conv.-step at 45MS/s. The core area is 0.072mm2.

We present the design, implementation, and evaluation of μmedIC, a fully-integrated wireless and batteryless micro-implanted sensor. The sensor powers up by harvesting energy from RF signals and communicates at near-zero power via backscatter. In contrast to prior designs which cannot operate across various in-body environments, our sensor can self-reconfigure to adapt to different tissues and channel conditions. This adaptation is made possible by two key innovations: a reprogrammable antenna that can tune its energy harvesting resonance to surrounding tissues, and a backscatter rate adaptation protocol that closes the feedback loop by tracking circuit-level sensor hints.