Verification and debug of AI is a multi-level problem with several stakeholders, each with different tools and responsibilities.
When an AI algorithm is deployed in the field and gives an unexpected result, it’s often not clear whether that result is correct.
So what happened? Was it wrong? And if so, what caused the error? These are often not simple questions to answer. Moreover, as with all verification problems, the only way to get to the root cause is to break the problem down into manageable pieces.
The semiconductor industry has faced similar problems in the past. Software runs on hardware, and the software must assume the hardware will never make an error. Similarly, when mapping hardware onto an FPGA, it is expected that the FPGA fabric will never make a mistake. In both cases, the underlying execution platforms have been verified to an extent where they built trust over time, and the amount of verification performed on them in some cases is monumental…
To read the full Semiconductor Engineering article by Brian Bailey, click here.