Proof-of-useful-work (PoUW), an alternative to the widely used proof-of-work (PoW), aims to re-purpose the network’s computing power. Namely, users evaluate meaningful computational problems, e.g., solving optimization problems, instead of computing numerous hash function values as in PoW. A recent approach utilizes the training process of deep learning as “useful work”. However, these works lack security analysis when deploying them with blockchain-based protocols, let alone the informal and over-complicated system design. This work proposes a distributed proof-of-deep-learning (D-PoDL) scheme concerning PoUW’s requirements. With a novel hash-traininßg-hash structure and model-referencing mechanism, our scheme is the first deep learning-based PoUW scheme that enables achieving better accuracy distributively. Next, we introduce a transformation from the D-PoDL scheme to a generic D-PoDL blockchain protocol which can be instantiated with two chain selection rules, i.e., the longest-chain rule and the weight-based blockchain framework (LatinCrypt’ 21). This work is the first to provide formal proofs for deep learning-involved blockchain protocols concerning the robust ledger properties, i.e., chain growth, chain quality, and common prefix. Finally, we implement the D-PoDL scheme to discuss the effectiveness of our design.

We introduce a method for assigning photorealistic relightable materials to 3D shapes in an automatic manner. Our method takes as input a photo exemplar of a real object and a 3D object with segmentation, and uses the exemplar to guide the assignment of materials to the parts of the shape, so that the appearance of the resulting shape is as similar as possible to the exemplar. To accomplish this goal, our method combines an image translation neural network with a material assignment neural network. The image translation network translates the color from the exemplar to a projection of the 3D shape and the part segmentation from the projection to the exemplar. Then, the material prediction network assigns materials from a collection of realistic materials to the projected parts, based on the translated images and perceptual similarity of the materials. One key idea of our method is to use the translation network to establish a correspondence between the exemplar and shape projection, which allows us to transfer materials between objects with diverse structures. Another key idea of our method is to use the two pairs of (color, segmentation) images provided by the image translation to guide the material assignment, which enables us to ensure the consistency in the assignment. We demonstrate that our method allows us to assign materials to shapes so that their appearances better resemble the input exemplars, improving the quality of the results over the state-of-the-art method, and allowing us to automatically create thousands of shapes with high-quality photorealistic materials. Code and data for this paper are available at https://github.com/XiangyuSu611/TMT.

SRAM-based FPGA is more sensitive to multiple bit upset, and the possibility of accumulation of memory's upset is high. In order to improve the ability that SRAM-based FPGA is more stable to multiple upset, this paper presents a new type design of multiple errors correction. The design combines BCH(15,7) code which can correct two errors and the improved TMR technology and achieves detecting and correcting multiple bit upset. Compared with the classical Hamming codes and extended Hamming codes, it has the advantage of correcting multiple bit upset. And compared with the traditional TMR, it can effectively determine the validity of the data after voting. Meanwhile, the design writes back the right data when error happens to avoid the accumulation of errors.