--High-quality, multi-channel neural recording is indispensable for neuroscience research and clinical applications. Large-scale brain recordings often produce vast amounts of data that must be wirelessly transmitted for subsequent offline analysis and decoding, especially in brain-computer interfaces (BCIs) utilizing high-density intracortical recordings with hundreds or thousands of electrodes. However, transmitting raw neural data presents significant challenges due to limited communication bandwidth and resultant excessive heating. T o address this challenge, we propose a neural signal compression scheme utilizing Convolutional Autoencoders (CAEs), which achieves a compression ratio of up to 150 for compressing local field potentials (LFPs). The CAE encoder section is implemented on RAMAN, an energy-efficient tinyML accelerator designed for edge computing. Additionally, we employ hardware-software co-optimization by pruning the CAE encoder model parameters using a hardware-aware balanced stochastic pruning strategy, resolving workload imbalance issues and eliminating indexing overhead to reduce parameter storage requirements by up to 32.4%. Post layout simulation shows that the RAMAN encoder can be implemented in a TSMC 65-nm CMOS process, occupying a core area of 0.0187 mm Operating at a clock frequency of 2 MHz and a supply voltage of 1.2 V, the estimated power consumption is 15.1 µ W per channel for the proposed DS-CAE1 model. The compressed neural data from RAMAN is reconstructed offline with signal-to-noise and distortion ratios (SNDR) of 22.6 dB and 27.4 dB, along with R2 scores of 0.81 and 0.94, respectively, evaluated on two monkey neural recordings. A. Krishna is with the Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore - 560012, India, and also with the International Centre for Neuromorphic Systems, The MARCS Institute, Western Sydney University, Australia. S. Debnath, M. Srivatsav, M. Mehendale, and C. S. Thakur (Email: csthakur@iisc.ac.in) are with the Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore - 560012, India. A. van Schaik is with the International Centre for Neuromorphic Systems, The MARCS Institute, Western Sydney University, Australia. This work was supported by the Pratiksha Trust grant BCD - FG/SMCH-22-2106 and INAE grant INAE/121/AKF/48 (SAP code - SP/INAE-23-0001). BCIs have emerged as a revolutionary tool for advancing our understanding of the brain and are increasingly being utilized across various clinical applications [5]-[7], providing inventive solutions for communication [8], control [1], [9], and rehabilitation [10]-[13]. Ongoing improvements in signal processing, machine learning algorithms, and neurotechnology pave the way for BCIs to revolutionize healthcare, human-computer interaction, and beyond.