Value Proposition Design Ebook Download !EXCLUSIVE! Configuration Cheats
The functionality of FPGA can change upon every power-up of the device. This means that if a design engineer wants to make a change, they can simply download a new configuration file into the device and try out the change.
Value Proposition Design Ebook Download configuration cheats
ASIC (Application Specific Integrated Circuits) and FPGAs have different value propositions. What distinguishes FPGAs from ASIC is the fact that FPGA can be reprogrammed to a desired application or functionality requirements after manufacturing whereas ASIC is custom manufactured for specific design tasks. These reprogramming changes can occur during the PCB (Printed Circuit Board) assembly process, or even after the equipment has been shipped out to the customers.
Hyperparameter optimization is a key aspect of the lifecycle of machine learning applications. While methods such as grid search are incredibly effective for optimizing hyperparameters for specific isolated models, they are very difficult to scale across large permutations of models and experiments. A company like Facebook operates thousands of concurrent machine learning models that need to be constantly tuned. To achieve that, Facebook engineering teams need to regularly conduct A/B tests in order to determine the right hyperparameter configuration. Data in those tests is difficult to collect and they are typically conducted in isolation of each other which end up resulting in very computationally expensive exercises. One of the most innovative approaches in this area came from a team of AI researchers from Facebook who published a paper proposing a method based on Bayesian optimization to adaptively design rounds of A/B tests based on the results of prior tests.
The fundamental goal of Bayesian optimization when applied to hyperparameter optimization is to determine how valuable is an experiment for a specific hyperparameter configuration. Conceptually, Bayesian optimization works very efficiently for isolated models but its value proposition is challenged when used in scenarios running random experiments. The fundamental challenge is related to the noise introduced in the observations.