Researchers at Rensselaer Polytechnic Institute and IBM T.J. Watson Research Center have published a technical paper introducing REACH, a controller microarchitecture designed to reduce the overhead of error-correcting codes (ECC) in High-Bandwidth Memory (HBM) systems. The work, titled “REACH: Controller-Managed Long-Span ECC for HBM AI Inference,” addresses the rising cost of HBM by proposing stronger controller-level protection that supports a wider range of device error rates without incurring prohibitive performance penalties. The paper was released in September 2026 and is available as an arXiv preprint.
REACH inner codes for common errors
The core challenge identified by the authors is that long-span error-correcting codes, while offering stronger protection at comparable code rates, traditionally couple small memory accesses to span-wide state and require costly decoding at HBM bandwidth. REACH leverages the specific access patterns of Large Language Model (LLM) decode phases, which are read-dominated. In this setting, sequential reads allow for span aggregation, while sparse writes limit the traffic required for parity updates. The architecture uses established inner codes to correct common errors and identify unresolved chunks, reserving a long outer code specifically for known-erasure repair.
less controller area in simulation
. A separate full-interface sizing analysis supports a /s application target using ASAP7-synthesized kernels. At this analytical target, REACH’s nominal composition uses less controller area and less modeled power than the evaluated mean-work direct-long design, demonstrating the benefit of reserving long-span recovery for exceptional requests rather than applying it to every transaction.
HBM shortage drives 50% price hikes
This technical optimization arrives against a backdrop of intense market pressure on HBM supply and pricing. Reports indicate that HBM shortages have driven Chinese AI chip manufacturers to rely on gray markets, with prices for components like Huawei’s Ascend 950DT rising by up to 50% in two months. Simultaneously, Samsung is expanding its HBM share, with analysts projecting its market position to rise from 33% to 40% in the fourth quarter of 2026. These market conditions underscore the economic necessity of optimizing HBM efficiency through techniques like REACH, as memory costs become a dominant factor in AI accelerator pricing (TechNews).
Lowering TCO for AI inference workloads
The significance of REACH lies in its potential to lower the total cost of ownership for AI inference workloads by mitigating the performance and area overhead of ECC. As HBM becomes a critical bottleneck for AI accelerators, reducing the computational cost of error correction allows for more efficient use of the memory bandwidth. This is particularly relevant for inference tasks, which are read-heavy and sensitive to latency. By decoupling common error correction from complex long-span decoding, REACH offers a practical path to maintaining data integrity without sacrificing the throughput required for large-scale LLM deployment.
Complementing SK hynix Indiana fab expansion
This technical optimization complements broader industry efforts to secure HBM supply, such as SK hynix’s US expansion and Samsung’s yield improvements, by making existing HBM capacity more effective for AI applications. While SK hynix broke ground on a new Indiana fab to serve US AI customers starting in late 2029, current market dynamics show significant strain. The ability to extract more performance from existing HBM stacks through smarter controller management may be as critical as increasing physical capacity, particularly as demand for AI inference continues to outpace supply.