A real-time operating system (RTOs) is often used in embedded system, to structure the application code and to ensure that the deadlines are met by reacting on events by executing the functions within precise time. Most embedded systems are bound to real-time constraints with determinism and latency as a critical metrics. RTOs are generally implemented in software, increases computational overheads, jitter and memory footprint. Modern FPGA technology, enables the implementation of a full featured and flexible hardware based RTOs, which helps in reducing to greater extent these overheads even if not remove completely. Scheduling algorithms play an important role in the design of real-time systems. An Adaptive Fuzzy Inference System (FIS) based scheduler framework proposed in this article is based on the study and conclusion drawn from the research over the years in HW SW co-design domain. The proposed novel two phase FIS based adaptive hardware task scheduler minimizes the processor time for scheduling activity which uses fuzzy logic to model the uncertainty at first stage along with adaptive framework that uses feedback which allows processors share of task running on multiprocessor to be controlled dynamically at runtime. This Fuzzy logic based adaptive hardware scheduler breakthroughs the limit of the number of total task and thus improves efficiency of the entire real-time system. The increased computation overheads resulted from proposed two phase FIS scheduler can be compensated by utilising the basic characteristics of parallelism of the hardware as scheduler being migrated to FPGA