This paper is an update of the survey of AI accelerators and processors from
past four years, which is now called the Lincoln AI Computing Survey - LAICS
(pronounced "lace"). As in past years, this paper collects and summarizes the
current commercial accelerators that have been publicly announced with peak
performance and peak power consumption numbers. The performance and power
values are plotted on a scatter graph, and a number of dimensions and
observations from the trends on this plot are again discussed and analyzed.
Market segments are highlighted on the scatter plot, and zoomed plots of each
segment are also included. Finally, a brief description of each of the new
accelerators that have been added in the survey this year is included.

AI 加速器和处理器的最新调查更新，收集和总结了当前公开宣布的商业加速器的性能和功耗数据，并分析了趋势和市场细分领域。

林肯人工智能计算调查 (LAICS) 更新

Lincoln AI Computing Survey (LAICS) Update

Item-to-Item (I2I) recommendation is an important function in most
recommendation systems, which generates replacement or complement suggestions
for a particular item based on its semantic similarities to other cataloged
items. Given that subsets of items in a recommendation system might be
co-interacted with by the same set of customers, graph-based models, such as
graph neural networks (GNNs), provide a natural framework to combine, ingest
and extract valuable insights from such high-order relational interactions
between cataloged items, as well as their metadata features, as has been shown
in many recent studies. However, learning GNNs effectively for I2I requires
ingesting a large amount of relational data, which might not always be
available, especially in new, emerging market segments. To mitigate this data
bottleneck, we postulate that recommendation patterns learned from existing
mature market segments (with private data) could be adapted to build effective
warm-start models for emerging ones. To achieve this, we propose and
investigate a personalized federated modeling framework based on GNNs to
summarize, assemble and adapt recommendation patterns across market segments
with heterogeneous customer behaviors into effective local models. Our key
contribution is a personalized graph adaptation model that bridges the gap
between recent literature on federated GNNs and (non-graph) personalized
federated learning, which either does not optimize for the adaptability of the
federated model or is restricted to local models with homogeneous
parameterization, excluding GNNs with heterogeneous local graphs.

本文提出了一种基于图神经网络 (GNNs) 的个性化联邦建模框架，用于在具有异构客户行为的市场部分之间总结、组装和适应推荐模式，从而缓解 I2I 数据瓶颈问题。