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ISSCC（International Solid-State Circuits Conference）国际固态电路会议由IEEE固态电路协会（SSCS）举办，是世界学术界和工业界公认的集成电路设计领域最顶尖的盛会，也被认为是“芯片奥林匹克”。始于1953年的ISSCC通常是各个时期国际上最尖端固态电路技术最先发表之地。每年吸引超过3000名来自世界各地工业界和学术界的参会者。
ISSCC 在技术领域方面历经变更，ISSCC2020为“机器学习及人工智能”新成立了独立的技术小组分会，至此，ISSCC的技术分类达到12个分类，包括模拟设计（ANA）、电源管理（PM）、无线传输（WLS）、数据转换器（DC）、前瞻技术（TD）、射频技术（RF）、数字电路（DCT）、图像、 MEMS、医疗、显示（IMMD）、以及机器学习和人工智能（ML）、存储（MEM）、有线传输（WLN）和数字系统（DAS）。
来自英伟达的Rangha Venkatesan讲解了关于加速深度神经网络设计的方法教程，值得关注。
深度神经网络有着广泛的应用。与通用处理器相比，该领域的定制硬件优化提供了显著的性能和功耗优势。然而，实现高的TOPS/W和/或TOPS/mm2以及对可伸缩性和可编程性的要求是一个挑战任务。这个本教程介绍了各种设计方法，以在不同神经网络和新模型的效率、可扩展性和灵活性之间取得正确的平衡。它介绍了（i）设计高效计算单元、内存层次结构和互连拓扑的不同电路和体系结构技术，（ii）有效平铺计算的编译器方法，以及（iii）在目标硬件上高效执行的神经网络优化。
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T7 - Basic Design Approaches to Accelerating Deep Neural Networks
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Blind face restoration (BFR) from severely degraded face images in the wild is a very challenging problem. Due to the high illness of the problem and the complex unknown degradation, directly training a deep neural network (DNN) usually cannot lead to acceptable results. Existing generative adversarial network (GAN) based methods can produce better results but tend to generate over-smoothed restorations. In this work, we propose a new method by first learning a GAN for high-quality face image generation and embedding it into a U-shaped DNN as a prior decoder, then fine-tuning the GAN prior embedded DNN with a set of synthesized low-quality face images. The GAN blocks are designed to ensure that the latent code and noise input to the GAN can be respectively generated from the deep and shallow features of the DNN, controlling the global face structure, local face details and background of the reconstructed image. The proposed GAN prior embedded network (GPEN) is easy-to-implement, and it can generate visually photo-realistic results. Our experiments demonstrated that the proposed GPEN achieves significantly superior results to state-of-the-art BFR methods both quantitatively and qualitatively, especially for the restoration of severely degraded face images in the wild. The source code and models can be found at https://github.com/yangxy/GPEN.
