As a chip design company, time-to-market and engineering efficiency are the most critical and expensive metrics upon which to focus. With this in mind, the team at Annapurna Labs chose Altair Accelerator™ as their job scheduler for front-end and back-end workflows. As a feature within Accelerator, Rapid Scaling unused compute resources was developed with Annapurna Labs to add structure and efficiency to scaling AWS compute resources, shorten time to results, and change the development model to Continuous Integration.

There's perhaps no industry more competitive than electronics design and manufacturing, and the semiconductor industry relies on expensive EDA tools that must be utilized efficiently for maximum value and performance. As Moore's law has stalled and designs grow in size and complexity, organizations are looking for new ways to speed up verification and regression testing. Increasingly, firms are turning to hardware-assisted verification technologies to simulate designs faster and speed up regression tests.

This paper explains some of the challenges associated with hardware verification and introduces Altair Hero – an enterprise job scheduler purpose-built for multi-vendor emulation environments. It also discusses techniques used to improve emulator efficiency and show how organizations can improve throughput and realize a better ROI on their emulator investments by leveraging advanced workload management.

VLSI engineering organizations manage workflows with high complexity dependency trees and simulation license availability constrains. Webinar viewers will understand how a carefully curated tool chain can help VLSI engineering teams optimize design processes for maximum performance and efficiency with critical success factors such as collaboration, efficient sharing of compute and license resources, clear visibility of progress and project status and reproducibility of results and concepts in mind.

Cloud computing is becoming an increasingly good choice for EDA, but a data-led transformation is needed to take advantage of the flexibility that public compute resources can offer and to maximize performance and cost. Cloud allows an organization to benefit from a clear return on investment that supports innovation and rapid prototyping, provided those advantages are fully exploited. The wins achievable by a well-planned hybrid cloud strategy should see reduced costs both on-premises and in the cloud. The ability to dynamically tune compute and storage resources based on business and application needs is only available in the cloud, and only if the right telemetry and data pipelines are in place to inform infrastructure decisions. In this paper we discuss how to put that plan in place and ensure that key business objectives are met as workflows are adapted and migrated to a new compute environment.