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Authentication between Electric Vehicle (EV) users and Battery Charging/Switching (BCS) stations is a significant problem for energy trading. Although there are many existing authentication schemes, cross-domain authentication for EV users and BCS stations from different domains (such as different regions, companies, manufacturers) has not been well addressed; several blockchain-based cross-domain authentication schemes have more than 11 times communication rounds and involve over 4 times exponentiation, scalar multiplications and bilinear pairing cryptographic operations; this leads to high communication and computational costs. This work aims to tackle this problem by using blockchain and certificate-based public-key cryptography. It proposes an authentication scheme that uses a blockchain ledger to record authentication materials across different domains. A two-stage smart contract is designed and deployed to verify the authentication materials before storing them permanently in the blockchain. A novel certificate-based authenticated key establishment (CB-AKE) protocol is proposed to authenticate EV users and BCS stations. Based on the theoretical analysis and practical evaluation on the security and performance, the proposed scheme can well address the cross-domain authentication problem with acceptable cost. Compared to benchmark schemes, the authentication latency of CB-AKE is decreased by at least 25% and the throughput of CB-AKE is increased by at least 34%.

Layer Pipeline (LP) represents an innovative architecture for neural network accelerators, which implements task-level pipelining at the granularity of layers. Despite improvements in throughput, LP architectures face challenges due to complicated dataflow design, intricate design space and high resource requirements. In this paper, we introduce an accelerator synthesis framework, CASCADE. CASCADE leverages a novel dataflow, CARD, to efficiently manage convolutional operations’ irregular memory access patterns using simplified logic and minimal buffers. It also employs advanced design space exploration methods to optimize unrolling parallelism and FIFO depth settings automatically for each layer. Finally, to further enhance resource efficiency, CASCADE leverages Lookup Table-based multiplication and accumulation units. With extensive experimental results, we demonstrate that CASCADE significantly outperforms existing works, achieving a $3\times $ improvement in resource efficiency and a $4\times $ improvement in power efficiency. It achieves over $1.5\times 10^{4}$ frames per second throughput and 71.9% accuracy on ImageNet.

Edge artificial intelligence applications impose rigorous demands on local hardware to improve throughput and energy efficiency. Computing-in-memory (CIM) architectures provide high parallel and energy-efficient solutions to accelerate the multiply-and-accumulate (MAC) operations in neural networks (NNs). While SRAM-based charge-domain CIM is achieving thousands of TOPS/W energy efficiency, it encounters limitations when dealing with full NN model deployments where both activations and weights are signed. This paper proposes an SRAM-based signed batch normalization (BN) CIM macro for supporting efficient bitwise sparse MAC computation with signed operands and BN operations in deep neural networks. The key features of this macro encompass: 1) a multibit weight unit for the optimization of bitstream sparsity and the sign bit computation, 2) a 2b-serial input configuration to increase throughput and the ADC energy amortization, and 3) a quantization-hardware co-design for the BN implementation. Measurement results show that the proposed 28 nm 64 Kb CIM macro achieves 16.38 TOPS throughput and 4.55 POPS/W energy efficiency, both normalized to 1b operands. The test accuracy of CIFAR10 is 92%, based on the ResNet18 model with co-design BN implementation at signed-8b precision activations and weights.

Periodontal ligament stem cells from deciduous teeth (DePDLSCs) can perceive and respond to mechanical signals upon exposure to various environments. The effects of mechanical stress on the biological characteristics and metabolism of DePDLSCs were investigated using in vitro stress loading.