Accelerated AI and ML Workloads refer to the use of specialized hardware accelerators, such as GPUs (Graphics Processing Units) or TPUs (Tensor Processing Units), to enhance the performance of artificial intelligence (AI) and machine learning (ML) tasks.

These accelerators are designed to handle the computational demands of training and inference processes more efficiently than traditional CPUs, leading to faster execution times and improved overall performance of AI and ML algorithms. This acceleration allows for quicker model training, more rapid inference, and the ability to tackle larger and more complex datasets, thereby advancing the capabilities of AI and ML applications across various domains.

• Bandwidth Bottlenecks: Accelerated AI and ML workloads often require high bandwidth memory to keep up with the massive amounts of data being processed. There's a need for memory architectures that can deliver higher bandwidth to match the computational capabilities of modern accelerators. This could involve innovations in DRAM design, such as wider memory buses, faster memory interfaces, or the integration of high-bandwidth memory (HBM) technologies.

• Latency Reduction: Latency, the time it takes for data to be accessed from memory, is critical for AI and ML workloads, especially for real-time applications like autonomous vehicles or natural language processing systems. There's a demand for low-latency storage solutions that can keep up with the fast-paced computations of accelerated AI and ML workloads.

• Scalability and Capacity: As AI and ML models continue to grow in complexity and size, the demand for memory capacity and scalability increases accordingly. There's a need for memory solutions that can scale to meet the growing demands of AI and ML workloads while maintaining reasonable cost-effectiveness. This could involve innovations in memory technologies to increase memory densities, as well as advancements in memory interconnects and system architectures to enable seamless scalability across multiple accelerators and storage devices.

• SMART's CXL AICs offer high-speed connectivity between accelerators, CPUs, and memory subsystems, leveraging the high-bandwidth capabilities of the CXL interface. Utilizing SMART's CXL AICs ensures that accelerated AI and ML workloads have access to sufficient memory resources to handle large datasets and complex models efficiently.

• SMART's CXL AICs can help minimize latency for accelerated AI and ML workloads by enabling the integration of high-speed storage solutions directly into the memory hierarchy. By leveraging the CXL interface's low-latency connectivity, organizations can connect SMART's CXL AICs to high-performance Flash storage devices like NVMe SSDs or persistent memory modules. This allows data to be stored and accessed with minimal latency, reducing the impact of data movement on overall system performance and accelerating AI and ML workloads.

• Organizations can scale their computing and storage infrastructure dynamically by adding SMART's CXL AICs to their existing systems, allowing them to meet the evolving demands of AI and ML workloads. Additionally, SMART's CXL AICs offer flexibility in terms of deployment options, supporting various server architectures and configurations to accommodate different workload requirements.