DreamRAM: A Fine-Grained Configurable Design Space Modeling Tool for Custom 3D Die-Stacked DRAM
By: Victor Cai , Jennifer Zhou , Haebin Do and more
Potential Business Impact:
Makes computer memory faster, bigger, and use less power.
3D die-stacked DRAM has emerged as a key technology for delivering high bandwidth and high density for applications such as high-performance computing, graphics, and machine learning. However, different applications place diverse and sometimes diverging demands on power, performance, and area that cannot be universally satisfied with fixed commodity DRAM designs. Die stacking creates the opportunity for a large DRAM design space through 3D integration and expanded total die area. To open and navigate this expansive design space of customized memory architectures that cater to application-specific needs, we introduce DreamRAM, a configurable bandwidth, capacity, energy, latency, and area modeling tool for custom 3D die-stacked DRAM designs. DreamRAM exposes fine-grained design customization parameters at the MAT, subarray, bank, and inter-bank levels, including extensions of partial page and subarray parallelism proposals found in the literature, to open a large previously-unexplored design space. DreamRAM analytically models wire pitch, width, length, capacitance, and scaling parameters to capture the performance tradeoffs of physical layout and routing design choices. Routing awareness enables DreamRAM to model a custom MAT-level routing scheme, Dataline-Over-MAT (DLOMAT), to facilitate better bandwidth tradeoffs. DreamRAM is calibrated and validated against published industry HBM3 and HBM2E designs. Within DreamRAM's rich design space, we identify designs that achieve each of 66% higher bandwidth, 100% higher capacity, and 45% lower power and energy per bit compared to the baseline design, each on an iso-bandwidth, iso-capacity, and iso-power basis.
Similar Papers
RACAM: Enhancing DRAM with Reuse-Aware Computation and Automated Mapping for ML Inference
Hardware Architecture
Makes AI models run much faster inside computer memory.
OpenGCRAM: An Open-Source Gain Cell Compiler Enabling Design-Space Exploration for AI Workloads
Hardware Architecture
Makes computer memory faster and use less power.
Towards Memory Specialization: A Case for Long-Term and Short-Term RAM
Hardware Architecture
Makes computers faster and cheaper using new memory.