DPUs and the value that Kalray DPUs can offer for CERN. 

A discussion about why DPUs are becoming so central in data intensive workloads – workloads that increasingly represent research and commercial applications.  Here are related topics we’d like to address:
 

• Specialized workloads that DPUs are designed to offload and accelerate such as networking, storage, AI, and security tasks.
• Hardware architectures that result in acceleration of these workloads - improving performance, efficiency, and scalability compared to using general-purpose CPUs alone. We can touch on additional benefits such as how DPUs optimize system resources, reduce latency, and offer tailored functionality for specialized tasks.
• Energy efficiency when performing specific tasks offloaded to DPUs. DPUs can process large amounts of data in parallel, reducing the time required to complete computations and conserving energy. This efficiency is particularly advantageous for tasks like machine learning inference where it’s crucial to be able to process vast amounts of data efficiently.
• Finally, we’d like to explore why DPUs are replacing FPGAs in many applications and scenarios largely due to DPU software programmability:
• Flexibility and adaptability 
• Rapid prototyping and development cycles compared to ASIC-based FPGAs 
• Lower development costs compared to ASIC-based FPGAs that require custom hardware design and fabrication; DPUs eliminate the need for costly ASIC development, making them more cost-effective for many applications
• System-level integration working alongside CPUs and other components