As large‑model inference demand outpaces training, the decode stage hits a memory‑wall that GPUs cannot efficiently cross; SRAM’s on‑chip bandwidth and low‑energy access open a path forward, though capacity and process limits still pose challenges.
The article examines the growing bottlenecks of traditional compute architectures, explains why breaking the storage wall through high‑bandwidth communication, near‑data processing, and in‑memory compute is essential for AI workloads, and surveys the principles, advantages, challenges, future directions, and key industry players of integrated compute‑storage chips.
The article analyzes the growing bottlenecks in compute architecture and memory, explores high‑bandwidth communication, near‑data processing, and in‑memory computing techniques, evaluates their advantages, challenges, and future prospects, and highlights key industry players driving the shift toward integrated compute‑storage chips.
