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Hyperdimensional computing (HDC) has emerged as a promising paradigm offering lightweight yet powerful computing capabilities with inherent learning characteristics. By leveraging binary hyperdimensional vectors, HDC facilitates efficient and robust data processing, surpassing traditional machine learning (ML) approaches in terms of both speed and resilience. This letter addresses key challenges in HDC systems, particularly the conversion of data into the hyperdimensional domain and the integration of HDC with conventional ML frameworks. We propose a novel solution, the hyperdimensional vector quantized variational auto encoder (HDVQ-VAE), which seamlessly merges binary encodings with codebook representations in ML systems. Our approach significantly reduces memory overhead while enhancing training by replacing traditional codebooks with binary (−1, +1) counterparts. Leveraging this architecture, we demonstrate improved encoding-decoding procedures, producing high-quality images within acceptable peak signal-to-noise ratio (PSNR) ranges. Our work advances HDC by considering efficient ML system deployment to embedded systems.