Why CPUs and GPUs Struggle with AI and How Specialized AI Chips Are Changing the Game

To meet diverse intelligent‑computing tasks, AI chips have evolved into three main branches: large‑scale network training chips, high‑performance general‑purpose AI chips, and specialized chips for edge devices such as robots.

Conventional CPUs, built on the von‑Neumann architecture, suffer from heavy instruction‑fetch, decode, register‑access and data‑write‑back overhead, limiting performance, energy efficiency, and scalability for AI workloads.

GPUs improve parallel compute density but are constrained by power consumption and heat dissipation, making them unsuitable for many edge scenarios.

Specialized AI chips—ASICs and FPGAs—offer high compute‑per‑watt (100‑1000 GOP/W) and can be tailored to specific algorithms. ASICs deliver fixed high performance but have long design cycles and limited flexibility, while FPGAs provide reconfigurable hardware that combines ASIC‑level efficiency with adaptability and lower development cost.

Key industry examples include Google’s TPU (Tensor Processing Unit) optimized for matrix multiplication and convolution, NVIDIA’s high‑end GPUs (e.g., RTX 4090) with thousands of CUDA cores, Qualcomm’s ZeroTh chips for low‑power mobile AI, and Chinese companies such as DeePhiTech and DeepInsight that integrate NPU cores or FPGA‑based designs for robot perception.

Challenges of General‑Purpose Chips

Heavy instruction‑fetch, decode, and register‑access overhead reduces performance and energy efficiency.

Separate storage and compute units cause costly data movement and latency.

Fixed datapath width hinders multi‑precision collaborative computation.

Advantages of ASICs

High performance and low power consumption (100‑1000 GOP/W).

Excellent reliability and integration for edge AI.

However, ASICs suffer from long design cycles, limited extensibility, and inability to adapt to evolving neural‑network architectures.

Advantages of FPGAs

Reconfigurable hardware eliminates the need for fixed instruction pipelines, drastically reducing power consumption.

Flexibility allows algorithm‑level optimizations and rapid adaptation to new models.

Development cost is far lower than ASICs.

These benefits have led to a shift from ASIC‑dominant designs toward FPGA‑based accelerators in many high‑performance and edge applications.

Emerging Trends in Reconfigurable AI Chips for Robotics

Recent research demonstrates FPGA‑based pipelines (e.g., binary‑CNN YOLOv2 on Xilinx ZCU102) achieving 40.81 fps—177× faster than ARM Cortex‑A57 and 27.5× faster than an embedded NVIDIA Pascal GPU—showcasing the potential of reconfigurable AI chips for robot perception tasks such as object detection, depth estimation, and optical flow.

Overall, specialized AI chips are becoming indispensable for robot intelligent computing, offering high compute density, low power, and flexible data‑flow architectures that address the shortcomings of traditional CPUs and GPUs.