The energy and latency of an accelerator running a deep neural network (DNN) depend on how the computation and data movement are scheduled in the accelerator (i.e., mapping), and picking an optimal mapping is essential to achieve high-performance accelerators. However, it is challenging to find mappings that maximize accelerator performance. The space of mappings is large, and prior works cannot guarantee finding optimal mappings because they use heuristics or metaheuristics to narrow the search space. To address this challenge, we propose the Turbo-Charged Mapper (TCM), a fast mapper that finds optimal mappings. The key to our approach is that we define a new mapping concept called dataplacement, which, like the prior concept of dataflow, allows for clear analysis and comparison of mappings. Through it, we identify opportunities to prune redundant and suboptimal mappings, reducing search space by up to 32 orders of magnitude ($10^{37}\rightarrow10^5$). TCM leverages these insights to perform full mapspace searches, making it the first mapper that can find optimal mappings in feasible runtime. Compared to prior mappers, TCM improves accelerator energy-delay-product by $1.2-6.5\times$ while simultaneously reducing mapping search time by $1000\times$ (5 hours $\rightarrow$ 17 seconds).

翻译：加速器运行深度神经网络（DNN）的能耗与延迟取决于计算及数据在加速器内的调度方式（即映射），选择最优映射对于实现高性能加速器至关重要。然而，寻找能最大化加速器性能的映射极具挑战性。由于映射空间庞大，现有方法因采用启发式或元启发式算法缩小搜索空间，无法保证找到最优映射。针对这一难题，我们提出增压映射器（Turbo-Charged Mapper, TCM）——一种能快速发现最优映射的映射器。本方法的核心在于定义了名为数据放置（dataplacement）的新映射概念，该概念与先前的数据流（dataflow）概念类似，能够清晰分析和比较映射方案。通过该概念，我们识别出剪枝冗余与次优映射的机会，将搜索空间缩减最多32个数量级（$10^{37}\rightarrow10^5$）。TCM利用这些洞察完成全映射空间搜索，成为首个能在可行运行时间内找到最优映射的映射器。与现有映射器相比，TCM在将映射搜索时间降低$1000$倍（5小时→17秒）的同时，使加速器能耗延迟积改善$1.2-6.5$倍。