WACT: Lossless Compression of INT8 Weights and Activations to Reduce Data Traffic in Edge NPU Memory Hierarchies

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Data movement constrains edge convolutional neural network (CNN) accelerators. We present Weight and Activation Compression for Tensor Traffic (WACT), a lossless framework for 8-bit signed integer (INT8) weights and consumer-facing activations. WACT calibrates a mode, tile geometry, and Rice parameter per invocation-aware tensor identity, freezes the policy, and uses a complete metadata- and alignment-inclusive comparison with strict uncompressed (RAW) fallback. Across five image-classification CNNs with one cold weight load and 320 activation passes per model, WACT reduced measured downstream occupied packet bytes by 44.48% (1.801×) over 78,339 events, with zero decoded-INT8 mismatches. A minimal aligned-RAW sensitivity changed this saving by 0.0293 percentage points. On MobileNetV2, FULL_WACT exceeded the best recalibrated single mode and a matched zero-value-compression-style baseline by at least 5.402 and 8.587 percentage points, respectively. A separate labeled 50,000-image validation measured FP32 and INT8 quantize–dequantize (QDQ) Top-1 accuracies of 72.148% and 71.392%, respectively; the latter used scales frozen from the canonical 32-image calibration subset, denoted S0. Explicitly bounded aggregate cold-start SCALE-Sim modeled-access projections were 11.00–17.70%. The complete WACT policy was evaluated in software, whereas the implemented hardware was limited to the single-tile Rice-mode encoder and decoder paths. The routed 28 nm CMOS blocks had standard-cell areas of 0.010942 and 0.010571 mm2, passed post-layout SDF loopback, and consumed 4.939 mW of codec-logic power. The measured downstream-byte reduction therefore provides an energy-saving opportunity whose realization depends on codec overhead and the cost of memory movement in the target hierarchy.