  Customized hardware accelerators have been developed to provide improved
performance and efficiency for DNN inference and training. However, the
existing hardware accelerators may not always be suitable for handling various
DNN models as their architecture paradigms and configuration tradeoffs are
highly application-specific. It is important to benchmark the accelerator
candidates in the earliest stage to gather comprehensive performance metrics
and locate the potential bottlenecks. Further demands also emerge after
benchmarking, which require adequate solutions to address the bottlenecks and
improve the current designs for targeted workloads. To achieve these goals, in
this paper, we leverage an automation tool called DNNExplorer for benchmarking
customized DNN hardware accelerators and exploring novel accelerator designs
with improved performance and efficiency. Key features include (1) direct
support to popular machine learning frameworks for DNN workload analysis and
accurate analytical models for fast accelerator benchmarking; (2) a novel
accelerator design paradigm with high-dimensional design space support and
fine-grained adjustability to overcome the existing design drawbacks; and (3) a
design space exploration (DSE) engine to generate optimized accelerators by
considering targeted AI workloads and available hardware resources. Results
show that accelerators adopting the proposed novel paradigm can deliver up to
4.2X higher throughput (GOP/s) than the state-of-the-art pipeline design in
DNNBuilder and up to 2.0X improved efficiency than the recently published
generic design in HybridDNN given the same DNN model and resource budgets. With
DNNExplorer's benchmarking and exploration features, we can be ahead at
building and optimizing customized AI accelerators and enable more efficient AI
applications.
