Managed-Retention Memory: A New Class of Memory for the AI Era

AI clusters today are one of the major uses of High Bandwidth Memory (HBM). However, HBM is suboptimal for AI workloads for several reasons. Analysis shows HBM is overprovisioned on write performance, but underprovisioned on density and read bandwidth, and also has significant energy per bit overheads. It is also expensive, with lower yield than DRAM due to manufacturing complexity. We propose a new memory class: Managed-Retention Memory (MRM), which is more optimized to store key data structures for AI inference workloads. We believe that MRM may finally provide a path to viability for technologies that were originally proposed to support Storage Class Memory (SCM). These technologies traditionally offered long-term persistence (10+ years) but provided poor IO performance and/or endurance. MRM makes different trade-offs, and by understanding the workload IO patterns, MRM foregoes long-term data retention and write performance for better potential performance on the metrics important for these workloads.

High-bandwidth Close-Range Information Transport through Light Pipes

Image retrieval after propagation through multi-mode fibers is gaining attention due to their capacity to confine light and efficiently transport it over distances in a compact system. Here, we propose a generally applicable information-theoretic framework to transmit maximal-entropy (data) images and maximize the information transmission over sub-meter distances, a crucial capability that allows optical storage applications to scale and address different parts of storage media. To this end, we use millimeter-sized square optical waveguides to image a megapixel 8-bit spatial-light modulator. Data is thus represented as a 2D array of 8-bit values (symbols). Transmitting 100000s of symbols requires innovation beyond transmission matrix approaches. Deep neural networks have been recently utilized to retrieve images, but have been limited to small (thousands of symbols) and natural looking (low entropy) images. We maximize information transmission by combining a bandwidth-optimized homodyne detector with a differentiable hybrid neural-network consisting of a digital twin of the experiment setup and a U-Net. For the digital twin, we implement and compare a differentiable mode-based twin with a differentiable ray-based twin. Importantly, the latter can adapt to manufacturing-related setup imperfections during training which we show to be crucial. Our pipeline is trained end-to-end to recover digital input images while maximizing the achievable information page size based on a differentiable mutual-information estimator. We demonstrate retrieval of 66 kB at maximum with 1.7 bit per symbol on average with a range of 0.3 - 3.4 bit.