In large-scale analytics, in-storage processing (ISP) can significantly boost query performance by letting ISP engines (e.g., FPGAs) preselect only the relevant data before sending them to databases.This reduces the amount of not only data transfer between storage and host, but also database computation, facilitating faster query processing.However, existing ISP solutions cannot effectively support a wide range of modern analytical queries because they only support simple combinations of frequently used operators (e.g., =, <), particularly on fixed-length columns.As modern databases allow filter predicates to include numerous operators/functions (e.g., dateadd) compatible with diverse data formats (and their complex combinations), it becomes more challenging for existing approaches to accelerate such queries efficiently.To address the limitations, we propose a new ISP approach, called Universal Predicate Pushdown (UPP), that can accelerate modern analytical databases, leveraging hardware/software co-design for a high level of flexibility.Our core insight is that instead of programming for individual filter operators/functions, we should devise a compact instruction set architecture (ISA) tailored explicitly for predicate pushdown.The software (i.e., database) layer recognizes and compiles various general filters (called a universal predicate) to a set of UPP-compliant instructions, which are then processed efficiently by FPGA using bitwise comparisons, leveraging lightweight metadata.In our experiments with a 100 GB TPC-H dataset, UPP running on SmartSSD could speed up Spark's end-to-end query performance by 1.2×-7.9×without changing input data formats.