AI workloads are massive, demanding a significant amount of bandwidth and processing power. As a result, AI chips require a unique architecture consisting of the optimal processors, memory arrays, security, and real-time data connectivity. Traditional CPUs typically lack the processing performance needed, but are ideal for performing sequential tasks. GPUs, on the other hand, can handle the massive parallelism of AI’s multiply-accumulate functions and can be applied to AI applications. In fact, GPUs can serve as AI accelerators, enhancing performance for neural networks and similar workloads.
Multi-die architectures consisting of heterogeneous integration of multiple dies, or chiplets, in a single package are fast-becoming an ideal architecture for AI applications as well. Multi-die systems are an answer to the slowing of Moore’s law, providing advantages beyond what monolithic SoCs are capable of: accelerated, cost-effective scaling of system functionality with reduced risk and faster time to market.
Regardless of the chosen architecture, AI-driven chip design technologies are streamlining the design process for AI chips, enabling better PPA and engineering productivity to get designs to market faster.