Tag
The article discusses how rising DDR5 memory prices signal a broader memory bottleneck in AI, particularly the KV cache in softmax attention for LLMs, and highlights post-transformer architectures like linear attention and state space models that aim to reduce memory usage.
Die analysis of the Intel 8087 math coprocessor's fast bit shifter, exploring its architecture and role in floating-point operations.
The article analyzes the AI inference ASIC architectures of Groq, SambaNova, Tenstorrent, and Cerebras, highlighting Cerebras's unique wafer-scale engine design. It discusses the benefits of deterministic latency and high bandwidth for LLM inference, while noting challenges like yield, cost, and KV cache bottlenecks.