Researchers from MIT and NVIDIA have developed two techniques that can accelerate the processing of sparse tensors, a type of data structure used for high-performance computing. The techniques, called HighLight and Tailors/Swiftiles, can improve the performance and energy-efficiency of hardware accelerators designed for processing sparse tensors. HighLight can efficiently handle various sparsity patterns, while Tailors/Swiftiles allows for overbooking, maximizing the utilization of chip memory. The techniques significantly reduce computation, energy consumption, and off-chip memory traffic. The findings will be presented at the IEEE/ACM International Symposium on Microarchitecture.
Accelerating Sparse Tensors for Massive AI Models
Researchers from MIT and NVIDIA have developed two techniques to improve the performance and energy-efficiency of systems that use sparse tensors, a type of data structure used in high-performance computing tasks and machine learning models. By efficiently exploiting sparsity (zero values) in tensors, these techniques can significantly enhance the processing of massive AI models.
Technique 1: Efficiently Finding Zero Values
The researchers created a hardware accelerator called HighLight, which can handle a wide variety of sparsity patterns and effectively find nonzero values in tensors. They achieved this by using hierarchical structured sparsity, which divides the tensor into smaller blocks with their own simple sparsity patterns. HighLight can skip zeros and take full advantage of this optimization opportunity, making it about six times more energy-efficient than other approaches.
Technique 2: Effectively “Overbooking” to Accelerate Workloads
To maximize the utilization of on-chip memory and reduce off-chip memory traffic, the researchers introduced the concepts of “overbooking” and “tailoring.” By choosing larger tile sizes and bumping out excess data, they optimized the processing of sparse tensors. They developed Swiftiles, a method that swiftly estimates the ideal tile size based on a specific percentage of overbooking. This technique more than doubles the speed and reduces energy demands compared to existing hardware accelerators.
Practical Applications of AI Solutions
If you want to evolve your company with AI and stay competitive, consider implementing AI solutions that efficiently accelerate sparse tensors for massive AI models. Here’s how you can get started:
1. Identify Automation Opportunities: Locate key customer interaction points that can benefit from AI.
2. Define KPIs: Ensure your AI endeavors have measurable impacts on business outcomes.
3. Select an AI Solution: Choose tools that align with your needs and provide customization.
4. Implement Gradually: Start with a pilot, gather data, and expand AI usage judiciously.
For AI KPI management advice, connect with us at hello@itinai.com. Stay tuned on our Telegram t.me/itinainews or Twitter @itinaicom for continuous insights into leveraging AI.
Spotlight on a Practical AI Solution: AI Sales Bot
Consider using the AI Sales Bot from itinai.com/aisalesbot to automate customer engagement and manage interactions across all customer journey stages. Discover how AI can redefine your sales processes and customer engagement. Explore solutions at itinai.com.
