Reservoir computing (RC) is a promising implementation of a non-von Neumann computing architecture. It is well-suited to solve time-dependent and dynamic problems like speech recognition or bitwise operations . In principal integrated photonic RC offers energy-efficient, high-speed signal processing capabilities [1,2]. However, current photonic RC systems perform the linear combination of the complex reservoir states in software. Software weights are ideal (high precision, no drift), but limit the processing speed and energy efficiency of the RC system . Here, we discuss a hardware implementation of photonic weights compatible with silicon photonic RC architectures. First, we demonstrate non-volatile synaptic weights based on ferroelectric barium titanate (BTO) thin films. Second, we explore how imperfections in these hardware weights impact the reservoir performance.