How Chip Solutions are Powering Edge AI Applications?

Learn how chip solutions are empowering edge AI applications

General-purpose chips are those that can run various types of AI algorithms and tasks, such as CPUs, GPUs, and FPGAs. These chips are widely available and can offer flexibility and scalability for edge AI applications. However, they may also have limitations in terms of performance, efficiency, and cost for some specific AI tasks.

AMD: Versal AI Edge system-on-chips from AMD is addressing edge AI potential and is intended to deliver competitive performance per watt against GPUs for real-time systems in autonomous vehicles, healthcare systems, factories, and aircraft.

Axelera AI: This is a startup company that is developing a chip solution for edge AI applications that combines a CPU, a GPU, an NPU, and an FPGA on a single chip. The company claims that its chip solution can offer high performance, low power consumption, and low latency for various edge AI applications.

Intel: This is a leading semiconductor company that is producing various chip solutions for edge AI applications, such as the Movidius Myriad X (NPU), the Intel Neural Compute Stick 2 (NPU), the Intel Atom x6000E Series (CPU), and the Intel Agilex (FPGA). The company also offers software tools and frameworks to support edge AI development.

Nvidia: This is a leading semiconductor company that produces various chip solutions for edge AI applications, such as the Nvidia Jetson Nano (GPU), the Nvidia Jetson Xavier NX (GPU), the Nvidia EGX (GPU), and the Nvidia BlueField-2 (DPU). The company also offers software tools and frameworks to support edge AI development.

SiMa.ai: This is a startup company that is developing a chip solution for edge AI applications that combines a CPU and a specialized machine learning processor (MLP) on a single chip. The company claims that its chip solution can offer high performance, low power consumption, and high accuracy for various edge AI applications.