Spectrum sensing in wireless communications serves as a crucial binary classification tool in cognitive radios, facilitating the detection of available radio spectrums for secondary users, especially in scenarios with high Signal-to-Noise Ratio (SNR). Leveraging Liquid State Machines (LSMs), which emulate spiking neural networks like the ones in the human brain, prove to be highly effective for real-time data monitoring for such temporal tasks. The inherent advantages of LSM-based recurrent neural networks, such as low complexity, high power efficiency, and accuracy, surpass those of traditional deep learning and conventional spectrum sensing methods. The architecture of the liquid state machine processor and its training methods are crucial for the performance of an LSM accelerator. This thesis presents one such LSM-based accelerator that explores novel architectural improvements for LSM hardware. Through the adoption of triplet-based Spike-Timing-Dependent Plasticity (STDP) and various spike encoding schemes on the spectrum dataset within the LSM, we investigate the advantages offered by these proposed techniques compared to traditional LSM models on the FPGA. FPGA boards, known for their power efficiency and low latency, are well-suited for time-critical machine learning applications. The thesis explores these novel onboard learning methods, shares the results of the suggested architectural changes, explains the trade-offs involved, and explores how the improved LSM model's accuracy can benefit different classification tasks. Additionally, we outline the future research directions aimed at further enhancing the accuracy of these models. / Master of Science / Machine Learning (ML) and Artificial Intelligence (AI) have significantly shaped various applications in recent years. One notable domain experiencing substantial positive impact is spectrum sensing within wireless communications, particularly in cognitive radios. In light of spectrum scarcity and the underutilization of RF spectrums, accurately classifying spectrums as occupied or unoccupied becomes crucial for enabling secondary users to efficiently utilize available resources. Liquid State Machines (LSMs), made of spiking neural networks resembling human brain, prove effective in real-time data monitoring for this classification task. Exploiting the temporal operations, LSM accelerators and processors, facilitate high performance and accurate spectrum monitoring than conventional spectrum sensing methods. The architecture of the liquid state machine processor's training and optimal learning methods plays a pivotal role in the performance of a LSM accelerator. This thesis delves into various architectural enhancements aimed at spectrum classification using a liquid state machine accelerator, particularly implemented on an FPGA board. FPGA boards, known for their power efficiency and low latency, are well-suited for time-critical machine learning applications. The thesis explores onboard learning methods, such as employing a targeted encoder and incorporating Triplet Spike Timing-Dependent Plasticity (Triplet STDP) in the learning reservoir. These enhancements propose improvements in accuracy for conventional LSM models. The discussion concludes by presenting results of the architectural implementations, highlighting trade-offs, and shedding light on avenues for enhancing the accuracy of conventional liquid state machine-based models further.