GPU-64: A 64-bit Inference GPU with Native O(1) KV-Cache for Edge LLM Deployment

GPU-64 is a power-efficient 64-bit GPU architecture optimized for Large Language Model (LLM) inference.

Key innovations:
- Content-Addressable Memory (CAM) based KV-Cache with O(1) lookup latency
- 16,384 KV entries per SM (4× more than GPU-256)
- 8×8 tensor cores for FP16/INT8 inference
- 75W TDP for edge/mobile deployment
- 4× inference speedup vs traditional GPUs

The architecture uses compact 64-bit registers (KEY[32] + VALUE[32]) enabling 4× more KV-Cache entries compared to GPU-256, making it ideal for long-context LLM inference on edge devices.

RTL implementation and Python emulator available on GitHub.