  We present MicroGrad, a centralized automated framework that is able to
efficiently analyze the capabilities, limits and sensitivities of complex
modern processors in the face of constantly evolving application domains.
MicroGrad uses Microprobe, a flexible code generation framework as its back-end
and a Gradient Descent based tuning mechanism to efficiently enable the
evolution of the test cases to suit tasks such as Workload Cloning and Stress
Testing. MicroGrad can interface with a variety of execution infrastructure
such as performance and power simulators as well as native hardware. Further,
the modular 'abstract workload model' approach to building MicroGrad allows it
to be easily extended for further use.
  In this paper, we evaluate MicroGrad over different use cases and
architectures and showcase that MicroGrad can achieve greater than 99\%
accuracy across different tasks within few tuning epochs and low resource
requirements. We also observe that MicroGrad's accuracy is 25 to 30\% higher
than competing techniques. At the same time, it is 1.5x to 2.5x faster or would
consume 35 to 60\% less compute resources (depending on implementation) over
alternate mechanisms. Overall, MicroGrad's fast, resource efficient and
accurate test case generation capability allow it to perform rapid evaluation
of complex processors.
