Semiconductors
How architectural co-design of memory and compute elements reduces energy per operation in semiconductor systems.
A focused discussion on co-design strategies that tightly couple memory and computation, enabling data locality, reduced fetch energy, and smarter data movement to lower energy per operation across diverse semiconductor architectures.
July 16, 2025 - 3 min Read
In modern semiconductor systems, energy efficiency hinges on more than faster transistors; it depends on data movement and the alignment of memory with compute. Co-design prompts engineers to rethink interfaces, hierarchies, and local storage so information travels shorter distances and operations exploit data locality. By integrating memory closely with compute blocks, systems can minimize unnecessary copies, reduce memory access latencies, and orchestrate compute sequences that reuse data already resident in fast storage. This approach often trades some raw peak memory capacity for dramatic gains in energy efficiency, leveraging specialized memory blocks that match the cadence of processors and the demands of targeted workloads. The result is richer performance per watt.
Architectural co-design begins by mapping data flows onto hardware tiles where memory and compute resources share quasi-physical proximity. Designers explore heterogeneous memories, near-memory processing, and compute-in-memory concepts that blur the line between storage and calculation. In practice, this means structuring caches, buffers, and scratchpads to feed arithmetic units with minimal delay and energy. The challenge lies in balancing flexibility with efficiency: wide applicability versus optimized pathways for common tasks. Early-stage modeling helps predict energy per operation under various data reuse patterns, guiding decisions about processor microarchitecture, memory density, and bandwidth provisioning. The payoff is sustained energy savings across representative workloads.
Integrate near-memory processing and compute-in-memory strategies.
When memory and compute are co-located, data no longer traverses long interconnect paths, and the cost of moving information shrinks noticeably. This shift enables more aggressive exploitation of data reuse, where the same data stay resident in fast-access memory across multiple operations. For software, this often translates to new strategies: organizing computations to maximize cache hits, preferring sequential access, and restructuring loops to keep active datasets warm. For hardware, it means designing layout-aware memory controllers, bank interleaving tuned to workload patterns, and interconnect topologies that minimize hop counts. Together, these choices minimize wasted energy associated with memory traffic and amplify the effectiveness of the compute engine.
A practical outcome of this co-design mindset is the creation of memory hierarchy trees tailored to specific workloads. Instead of a one-size-fits-all approach, designers select memory technologies—like multi-level caches, high-bandwidth memory, or compact scratchpads—that align with the temporal and spatial locality of target tasks. In such configurations, energy per operation drops because each step of a computation uses data that resides in the most appropriate tier, avoiding needless fetches from distant storage. Importantly, co-design encourages close collaboration between memory subsystem engineers and ISA, compiler, and microarchitecture teams, ensuring end-to-end efficiency from instruction formulations to physical data placements.
Design for data reuse, locality, and modern workloads.
Near-memory processing rethinks the separation between memory banks and processing units by situating simpler compute elements closer to memory. This architecture reduces the energy cost of data movement, because data travels shorter distances and fewer transistors switch during transfers. The trade-offs involve managing the heat footprint of memory-side computation, maintaining coherence across banks, and delivering sufficient parallelism to keep compute units occupied. Realizing benefits requires careful workload characterization: identifying data-parallel patterns that tolerate lower compute density but benefit from frequent data reuse. When successfully implemented, near-memory processing can dramatically lower energy per operation for workloads dominated by memory-bound phases, such as big data analytics and streaming inference.
Compute-in-memory approaches push computation directly into memory cells or in adjacent circuitry, eliminating the need to shuttle data back and forth across boundaries. The energy advantages accumulate when arithmetic operations are executed where the data resides, reducing costly transfers and exploiting memory bandwidth more effectively. Realizing these gains demands addressing programming model challenges: how to express a diverse set of operations in a near-memory fabric, how to map high-level abstractions to physical operations, and how to maintain reliability in dense, thermally constrained environments. If these hurdles are overcome, compute-in-memory becomes a powerful lever for reducing energy per operation in data-intensive engines.
Leverage cross-layer optimization from devices to data paths.
Beyond hardware boundaries, software tools play a pivotal role in maximizing co-design benefits. Compilers that understand memory topology can reorder computations to preserve locality, fuse operations to reduce intermediate data, and schedule tasks to exploit data living in fast memory layers. Profilers that capture energy metrics tied to memory access patterns empower developers to iterate quickly, pushing for layouts and transformations that shrink energy per operation. In practice, this means embracing memory-aware optimizations as a first-class concern, rather than a secondary afterthought. The synergy between software-aware scheduling and hardware-aware memory design is what unlocks meaningful energy reductions in real-world systems.
Another dimension is tiered memory management, where systems dynamically adapt memory allocation to workload phases. For instance, during latency-critical phases, the controller might elevate cache residency and prefetch aggressively, while during batch-oriented periods it prioritizes energy savings through deeper sleep states or lower-frequency operation. This adaptive strategy reduces average energy per operation by focusing resources where they matter most. Achieving it requires intelligent policies, hardware counters, and reliable prediction models to avoid performance cliffs or energy waste due to mispredictions. When executed well, tiered management sustains efficiency across varied operating conditions.
Real-world impact, metrics, and future directions.
Cross-layer optimization begins with a shared vocabulary of energy metrics that span device physics, architectural blocks, and software workloads. Establishing common benchmarks for energy per operation helps teams converge on feasible targets and tradeoffs. The next step involves crafting interfaces that expose memory bandwidth, latency, and non-volatile storage characteristics to the compiler and runtime system so decisions can be made with a holistic view. This visibility enables proactive scheduling and layout decisions, reducing stalls and unnecessary memory transitions. The outcome is a system that not only performs well but does so while consuming less energy per computation, even as workloads evolve.
In practice, cross-layer strategies encourage modular yet integrated design flows, where memory and compute blocks are developed with agreed APIs and performance envelopes. Hardware engineers prototype near-memory components in tandem with low-level microarchitectural features, while software teams implement abstractions that map cleanly to those capabilities. The resulting ecosystem makes it possible to pursue aggressive energy targets without compromising correctness or portability. As semiconductor technology advances, such collaborative engineering becomes essential to sustain gains in energy efficiency per operation across diverse applications.
Measuring energy per operation in integrated designs requires careful experimentation that isolates movement energy from compute energy, accounting for memory access patterns and thermal effects. Researchers emphasize metrics like data-traffic energy per byte, operational energy per multiply-accumulate, and average energy per memory access within a compute loop. By correlating these metrics with architectural choices—such as cache sizing, memory bank width, and interconnect topology—engineers gain actionable insights into where the biggest savings lie. The incremental improvements compound over time, enabling data centers to run denser workloads with smaller energy footprints and facilitating mobile devices with longer battery life without sacrificing performance.
Looking ahead, co-design will increasingly rely on simulation-driven design-space exploration, machine-learning-guided optimization, and programmable memories that adapt to evolving workloads. The future semi-conductor landscape favors architectures that seamlessly blend memory and compute in a way that minimizes energy per operation while staying robust to variability and aging. As manufacturing nodes continue to shrink, the importance of memory-centric strategies grows, making the co-design paradigm not merely advantageous but essential for sustainable progress in an era of ever-growing data processing demands. The vision is a family of systems where energy efficiency is baked into the core design philosophy, from silicon to software.
