https://nalar.dev/tags/model-compression/ Recent content in Model Compression on Nalar Hugo en-us Mon, 07 Sep 2026 00:00:00 +0700 https://nalar.dev/compress-neural-network-layers-with-low-rank-factorization/ Mon, 07 Sep 2026 00:00:00 +0700 https://nalar.dev/compress-neural-network-layers-with-low-rank-factorization/ <p>Large neural networks spend much of their memory and computation multiplying activations by weight matrices. Some of those matrices contain more independent structure than the model actually needs for a particular deployment. If so, we can approximate one large matrix with two smaller matrices and reduce the number of stored parameters and multiply-add operations.</p> <p>This technique is called <strong>low-rank factorization</strong>. The central idea is simple, but using it well requires more than choosing a smaller number. Compression changes the weights, approximation error can accumulate through a network, and fewer arithmetic operations do not guarantee lower wall-clock latency on every device.</p>