AI Hardware Accelerators: Architecture Trade-offs, Performance Analysis, and Production Deployment
Keywords:
AI accelerators, neural network hardware, TPU, GPU, FPGA, neuromorphic computing, inference optimization, hardware-software co-designAbstract
The exponential growth of artificial intelligence applications has created unprecedented demand for specialized hardware accelerators capable of efficiently processing complex neural network computations. This paper provides a comprehensive analysis of AI hardware accelerator architectures, examining critical design trade-offs between performance, power efficiency, and flexibility. We investigate the architectural evolution from general-purpose GPUs to domain-specific accelerators including TPUs, FPGAs, and neuromorphic processors. Through detailed performance analysis and real-world deployment case studies, we evaluate key metrics including throughput, latency, energy efficiency, and total cost of ownership. Our analysis reveals that while GPUs maintain dominance in training workloads, specialized ASICs demonstrate superior efficiency for inference tasks, achieving up to 10× better performance-per-watt. We examine production deployment challenges including model optimization, quantization strategies, and system integration considerations. The paper synthesizes current research trends and provides practical guidance for selecting appropriate accelerator architectures based on specific application requirements, workload characteristics, and deployment constraints.
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