Machine learning (ML) techniques have been applied to high-level synthesis (HLS) flows for quality-of-result (QoR) prediction and design space exploration (DSE). Nevertheless, the scarcity of accessible high-quality HLS datasets and the complexity of building such datasets present challenges. Existing datasets have limitations in terms of benchmark coverage, design space enumeration, vendor extensibility, or lack of reproducible and extensible software for dataset construction. Many works also lack user-friendly ways to add more designs, limiting wider adoption of such datasets. In response to these challenges, we introduce HLSFactory, a comprehensive framework designed to facilitate the curation and generation of high-quality HLS design datasets. HLSFactory has three main stages: 1) a design space expansion stage to elaborate single HLS designs into large design spaces using various optimization directives across multiple vendor tools, 2) a design synthesis stage to execute HLS and FPGA tool flows concurrently across designs, and 3) a data aggregation stage for extracting standardized data into packaged datasets for ML usage. This tripartite architecture ensures broad design space coverage via design space expansion and supports multiple vendor tools. Users can contribute to each stage with their own HLS designs and synthesis results and extend the framework itself with custom frontends and tool flows. We also include an initial set of built-in designs from common HLS benchmarks curated open-source HLS designs. We showcase the versatility and multi-functionality of our framework through six case studies: I) Design space sampling; II) Fine-grained parallelism backend speedup; III) Targeting Intel's HLS flow; IV) Adding new auxiliary designs; V) Integrating published HLS data; VI) HLS tool version regression benchmarking. Code at https://github.com/sharc-lab/HLSFactory.

Applications in the smart industry domain, such as interaction with collaborative robots using vocal commands or machine vision systems often requires the deployment of deep learning algorithms on heterogeneous low power computing platforms. The availability of software tools and frameworks to automatize different design steps can support the effective implementation of DL algorithms on embedded systems, reducing related effort and costs. One very important aspect for the acceptance of the framework, is its extensibility, i.e. the capability to accommodate different datasets and define customized preprocessing, without requiring advanced skills. The paper addresses a modular approach, integrated into the ALOHA tool flow, to support the data preprocessing and transformation pipeline. This is realized through customizable plugins and allows the easy extension of the tool flow to encompass new use cases. To demonstrate the effectiveness of the approach, we present some experimental results related to a keyword spotting use case and we outline possible extensions to different use cases.

As an engineering director leading research projects into the application of machine learning (ML) and deep learning (DL) to computational software for electronic design automation (EDA), I believe I have a unique perspective on the future of the electronic and electronic design industries. The next leap in design productivity for semiconductor chips and the systems built around them will come from the fusion of fully integrated EDA computational software tool flows, the application of distributed and multi-core computing on a broader scale and ML/DL. The current wave of artificial intelligence (AI) and ML innovation began with improved GPU computing capacity and the smart engineers who figured out how to harness it to accelerate deep neural network training. AI/ML will play a key role in the design of next-generation platforms, enabling the proliferation of today's technology drivers including 5G, hyperscale computing and others. In my role, the fun comes from the numerous non-deterministic polynomial (NP)-hard and NP-complete problems that exist at every stage of the design and verification process.

Videantis, which provides automotive deep learning, computer vision and video coding solutions, has announced that it will partner with the Fraunhofer Institute for Integrated Circuits IIS, Infineon and other leading companies and universities to develop an artificial intelligence (AI) ASIC and software development tools specifically for intelligent autonomous vehicles. The Videantis AI multi-core processor platform and tool flow has been selected for the KI-Flex autonomous driving chip project. Autonomous driving relies on fast and reliable processing and merging of data from several lidar, camera and radar sensors in the vehicle. This data can provide an accurate picture of the traffic conditions and environment to allow the vehicle to make intelligent decisions when driving. The process of intelligently analysing these volumes of sensor data requires high-performance, efficient, and versatile compute solutions.