Embedding compression techniques reduce the size of high-dimensional vector representations (embeddings) while preserving their usefulness in tasks like search, recommendation, or NLP. Common approaches include dimensionality reduction, quantization, pruning, and model architecture adjustments. These methods balance storage efficiency, computational speed, and accuracy trade-offs. Below, we’ll explore specific techniques and their practical applications.
Dimensionality reduction methods lower the number of dimensions in embeddings. Principal Component Analysis (PCA) is a classic example: it identifies the most informative axes in the data and projects embeddings onto these axes, discarding less important dimensions. For instance, reducing a 768-dimensional embedding to 128 dimensions can shrink storage by 83% with minimal performance loss. Another approach is feature hashing, which maps features to a fixed lower-dimensional space using hash functions. This is useful in scenarios like real-time recommendation systems where embeddings must be generated on-the-fly. However, hashing may introduce collisions, so techniques like multi-hashing or learned hash functions are often used to mitigate this.
Quantization reduces the numerical precision of embedding values. For example, converting 32-bit floating-point numbers to 8-bit integers cuts storage by 75% and speeds up computations. Scalar quantization involves dividing the range of embedding values into discrete buckets, while product quantization splits embeddings into subvectors and quantizes each separately, enabling efficient similarity search. In practice, Facebook’s FAISS library uses product quantization for billion-scale nearest-neighbor searches. Another variant is binary quantization, where embeddings are binarized (values set to 0 or 1), enabling ultra-fast bitwise operations. However, aggressive quantization can degrade accuracy, so hybrid approaches (e.g., retaining higher precision for critical dimensions) are common.
Pruning and model-based compression remove redundant information. Pruning eliminates less important embedding dimensions or weights, often by zeroing out small values and storing sparse representations. For example, in NLP models, 90% of embedding weights might be pruned with minimal impact on accuracy. Knowledge distillation trains a smaller “student” model to mimic a larger “teacher” model’s embeddings, effectively compressing the information. BERT-based models, for instance, often use distillation to create lightweight versions like TinyBERT. Additionally, parameter sharing (e.g., using the same embedding matrix across multiple model components) reduces redundancy. These techniques are particularly useful for deploying models on edge devices, where memory and compute constraints are tight.
Each technique has trade-offs: dimensionality reduction and quantization are fast but may lose fine-grained information, while model-based methods require retraining. Developers should experiment with combinations (e.g., PCA followed by quantization) and validate performance on their specific tasks. Libraries like TensorFlow Model Optimization or PyTorch’s dynamic quantization provide ready-to-use tools for implementing these strategies.