Framework

Overview

L2 Cache Layer: This layer is used to preload model weights, ensuring faster data access and reducing memory bottlenecks during AI inference tasks.
AVX-512 Execution Units: AVX-512 optimizes inference speed by enabling parallel processing, thus improving throughput and performance during intensive AI workloads.
Memory Controller: This component ensures efficient memory management, enabling high-speed data transfer and minimizing latency, which is essential for real-time AI applications.

Features

Low-Latency Inference: Marsha is designed to execute low-latency inference tasks directly on CPUs, making it ideal for real-time applications where response time is critical.
High Power Efficiency: Compared to GPUs, Marsha offers lower power consumption while delivering superior energy efficiency, making it more cost-effective for AI tasks in resource-constrained environments.

Technical Implementation

Marsha leverages the latest advancements in CPU architecture and specialized instruction sets to boost performance. The integration of AVX-512, AMX-TILE, and VNNI instructions allows Marsha to provide near-GPU performance without the need for additional hardware, significantly lowering the total cost of deployment and operational energy consumption.
By optimizing the L2 cache for weight preloading, Marsha ensures that large AI models can be loaded and processed faster, minimizing data access time and reducing inference delays.
The efficient memory controller minimizes the overhead of data transfer, ensuring that inference tasks are not hindered by slow memory access, which is especially important for real-time AI applications in embedded systems and edge computing.

Last updated 1 month ago

Overview

L2 Cache Layer: This layer is used to preload model weights, ensuring faster data access and reducing memory bottlenecks during AI inference tasks.
AVX-512 Execution Units: AVX-512 optimizes inference speed by enabling parallel processing, thus improving throughput and performance during intensive AI workloads.
Memory Controller: This component ensures efficient memory management, enabling high-speed data transfer and minimizing latency, which is essential for real-time AI applications.

Features

Low-Latency Inference: Marsha is designed to execute low-latency inference tasks directly on CPUs, making it ideal for real-time applications where response time is critical.
High Power Efficiency: Compared to GPUs, Marsha offers lower power consumption while delivering superior energy efficiency, making it more cost-effective for AI tasks in resource-constrained environments.

Technical Implementation

Marsha leverages the latest advancements in CPU architecture and specialized instruction sets to boost performance. The integration of AVX-512, AMX-TILE, and VNNI instructions allows Marsha to provide near-GPU performance without the need for additional hardware, significantly lowering the total cost of deployment and operational energy consumption.
By optimizing the L2 cache for weight preloading, Marsha ensures that large AI models can be loaded and processed faster, minimizing data access time and reducing inference delays.
The efficient memory controller minimizes the overhead of data transfer, ensuring that inference tasks are not hindered by slow memory access, which is especially important for real-time AI applications in embedded systems and edge computing.

Last updated 1 month ago