Accelerating the Design and Performance of Next Generation Computing Systems with GPUs
Sameer Halepete · 2022
The last few years have seen an accelerating growth in the demand for new silicon designs, even as the size and complexity of those designs has increased. However, the gains in design productivity necessary to implement these designs efficiently have not kept up. We need more than an order of magnitude increase in design productivity by the end of the decade to keep up with demand. Traditional methods for improving physical design tool capabilities are running out of steam, and there is a strong need for new approaches. Over the last two decades, we have seen other areas of computer science such as computer vision, speech recognition and natural language processing reach similar plateaus in performance, and each has been able to break out of the stall using GPU accelerated computing and machine learning. There is a similar opportunity in EDA but it will require a rethinking of the way these tools are implemented. The talk will cover where the demand for new silicon designs is coming from, what the productivity bottlenecks are, and then describe some advances in GPUs that could enable us to break through these bottlenecks with some examples.