- 3-10,000xestimated energy cost multiplier of moving data across a bus versus performing the arithmetic itself (the von Neumann bottleneck)
- 2264 TOPS/Wefficiency demonstrated by a heterogeneous analog-digital architecture with 2D mesh interconnects — 40-140x higher than GPUs
- 0single in-memory device technology that has emerged as a clear winner as of the most recent 2025 patent filings — RRAM, PCM, ferroelectric, and flash architectures all remain in active competition
Conventional processors, including GPUs, are built on the von Neumann architecture: a separate memory unit and a separate processing unit, connected by a bus that every piece of data has to cross before any arithmetic happens on it. That separation was a reasonable engineering trade-off for decades of general-purpose computing, but for AI workloads dominated by huge matrix multiplications, it means the chip spends a disproportionate share of its energy budget moving numbers back and forth rather than doing math with them. IBM Research and others have quantified just how disproportionate: moving data can cost anywhere from 3 to 10,000 times more energy than the arithmetic operation performed on it, a spread wide enough to make clear that for many AI workloads, the bus, not the multiplier, is where the energy actually goes.
How in-memory analog computing actually eliminates the bottleneck
Analog in-memory computing doesn’t optimize the bus — it removes the need to cross it for the core computation. The mechanism is elegantly simple physics: stored conductance values in a crossbar array of non-volatile memory cells multiply an applied voltage representing an input vector, and the resulting currents, summed along the array’s bitlines according to Kirchhoff’s current law, directly encode the result of a vector-matrix multiplication — the fundamental operation underlying nearly all neural network inference. That entire multiply-and-accumulate operation completes in a single clock cycle, inside the memory array itself, with no separate trip to a distant compute core required.
| Conventional (von Neumann) architecture | Analog in-memory computing | |
|---|---|---|
| Memory and compute | Physically separate, connected by a bus | Merged within the same physical substrate |
| Core operation | Data fetched, then multiplied in a separate compute unit | Multiplication and summation happen directly via Ohm’s Law and Kirchhoff’s current law in the memory array |
| Dominant energy cost | Moving data across the bus, often exceeding the arithmetic cost itself | Largely eliminated for the core vector-matrix multiplication |
| Precision | Exact digital arithmetic | Analog — subject to device noise and variability |
The technology landscape — still genuinely unsettled
Despite the appeal of the underlying physics, in-memory analog computing has evolved from instrumentation-era analog matrices into production-ready AI accelerators without a single winning device technology emerging. Resistive RAM (RRAM), phase-change memory (PCM), ferroelectric memory, and flash-based architectures are all actively competing as the underlying non-volatile memory cell, and as of the most recent 2025 patent filings, no single approach has established clear dominance. That’s a meaningfully different situation than, say, the HBM market, where the core technology direction is settled and the argument is about supply capacity — here, the fundamental device-level question is still open, which matters for anyone evaluating which architecture to bet on for a specific product timeline.
- 1. Von Neumann bottleneck identifiedMoving data between separate memory and compute units costs 3-10,000x more energy than the arithmetic itself.
- 2. Compute moves into memoryCrossbar arrays of non-volatile memory perform vector-matrix multiplication directly via physics (Ohm’s Law, Kirchhoff’s current law).
- 3. Multiple device technologies competeRRAM, PCM, ferroelectric, and flash-based cells all remain viable candidates with no clear winner yet.
- 4. Heterogeneous architectures show real gainsCombined analog-digital tile architectures demonstrate up to 140x the energy efficiency of a GPU on comparable workloads.
How analog in-memory computing structurally removes the data-movement cost
The 2264 TOPS/W figure comes from a heterogeneous architecture combining analog tiles with digital logic via a 2D mesh — not a pure analog design, reflecting the field’s current best-of-both-worlds approach.
The trade-off analog computing doesn’t escape
The physics that makes analog in-memory computing efficient — using continuous voltage and current rather than discrete digital states — is also its central limitation. Analog computation is inherently subject to device noise, cell-to-cell variability, and drift over time, none of which affect a conventional digital multiplier performing exact arithmetic. That makes analog in-memory computing a strong fit for inference workloads that can tolerate some numerical imprecision — most neural network inference falls into this category — and a poor fit for any computation requiring exact, reproducible digital results. This is why the technology is being positioned specifically as an AI inference accelerator, not a general-purpose computing replacement.
Deploy for noise-tolerant AI inference at the edge Strong fit
Right for battery- or power-constrained inference workloads where energy efficiency matters more than exact numerical reproducibility, and neural network robustness to small errors is well established.
Use heterogeneous analog-digital architectures for production accelerators Current best practice
Combining analog tiles for the bulk of matrix multiplication with digital logic for precision-critical operations, as demonstrated in the 2264 TOPS/W heterogeneous design, is the pragmatic middle path as of 2026.
Expect a pure analog design to replace general-purpose digital computing Not realistic
Analog computation’s inherent noise and variability make it unsuitable for exact, general-purpose arithmetic — this is an AI-inference-specific accelerator technology, not a broad computing replacement.
Common mistakes
Common mistake
Betting a product roadmap on a single in-memory device technology (RRAM, PCM, or otherwise) as though the field has settled. As of 2025 patent filings, no single approach has emerged as the clear winner, and choosing too early carries real technology-obsolescence risk.
Common mistake
Assuming analog in-memory computing can replace exact digital arithmetic wholesale. Device noise and variability make it well suited to noise-tolerant inference workloads and poorly suited to any computation requiring exact reproducibility.
What worked
Teams that got real efficiency gains adopted heterogeneous architectures — combining analog compute-in-memory tiles for the bulk of matrix multiplication with conventional digital logic for precision-sensitive operations — rather than betting entirely on a pure analog design before the underlying device technology question had settled.
Glossary
- Von Neumann bottleneck
- The energy and latency cost of moving data between physically separate memory and processing units, which can be 3 to 10,000 times more expensive than the arithmetic performed on that data.
- Crossbar array
- A grid structure of non-volatile memory cells used in analog in-memory computing, where stored conductance values and applied voltages combine via physics to compute a vector-matrix multiplication directly.
- Vector-matrix multiplication (VMM)
- The fundamental mathematical operation underlying most neural network computation, which analog in-memory computing performs directly within a memory crossbar array in a single clock cycle.
- TOPS/W
- Tera-operations per second per watt, a measure of computational energy efficiency used to compare accelerator architectures like analog in-memory computing against conventional GPUs.
Key takeaways
- The von Neumann bottleneck — moving data between separate memory and processing units — can cost 3 to 10,000 times more energy than the arithmetic operation performed on that data.
- Analog in-memory computing eliminates this cost structurally by performing vector-matrix multiplication directly within a crossbar array of non-volatile memory cells, using Ohm’s Law and Kirchhoff’s current law.
- Heterogeneous analog-digital architectures have demonstrated up to 2264 TOPS/W efficiency, roughly 40 to 140 times higher than conventional GPUs on comparable workloads.
- No single in-memory device technology — RRAM, PCM, ferroelectric, or flash-based — has emerged as a clear winner as of the most recent 2025 patent filings, leaving the field’s underlying technology direction genuinely unsettled.
- Analog computation’s reliance on continuous voltage and current makes it inherently subject to device noise and variability, unlike exact digital arithmetic.
- This makes analog in-memory computing well suited to noise-tolerant AI inference workloads specifically, not a general-purpose computing replacement.
- The pragmatic current best practice combines analog compute-in-memory tiles for bulk matrix multiplication with digital logic for precision-critical operations, rather than betting entirely on a pure analog design.
FAQs
What is the von Neumann bottleneck and why does it matter for AI chips?
It’s the energy and latency cost of moving data between physically separate memory and processing units, which can cost 3 to 10,000 times more energy than the arithmetic operation itself — a disproportionate cost for AI workloads dominated by huge matrix multiplications.
How does analog in-memory computing actually eliminate this bottleneck?
By performing computation directly within a crossbar array of non-volatile memory cells: stored conductance values multiply an applied voltage, and the resulting currents, summed via Kirchhoff’s current law, directly encode a vector-matrix multiplication result in a single clock cycle, with no separate trip to a distant compute core needed.
How much more efficient is analog in-memory computing than a GPU?
Heterogeneous analog-digital architectures have demonstrated up to 2264 TOPS/W efficiency, roughly 40 to 140 times higher than conventional GPUs on comparable AI workloads.
Which memory technology is winning in analog in-memory computing — RRAM, PCM, or something else?
None has established clear dominance yet. As of the most recent 2025 patent filings, RRAM, phase-change memory, ferroelectric memory, and flash-based architectures remain in active competition as the underlying device technology.
Can analog in-memory computing replace conventional digital processors entirely?
No. Its reliance on continuous voltage and current makes it inherently subject to device noise and variability, so it’s well suited to noise-tolerant AI inference workloads specifically, not to computation requiring exact, reproducible digital arithmetic.
Is analog in-memory computing already used in production AI accelerators?
It has evolved from instrumentation-era analog matrices into production-ready AI accelerators, typically using heterogeneous architectures that combine analog compute-in-memory tiles with conventional digital logic for precision-critical operations.
What is a crossbar array in analog in-memory computing?
It’s a grid of non-volatile memory cells whose stored conductance values and applied voltages combine via basic circuit physics to compute a vector-matrix multiplication directly within the memory itself, rather than requiring data to be fetched to a separate processor.
Should a company building an AI accelerator bet on analog in-memory computing today?
With caution regarding the specific device technology — since no single approach (RRAM, PCM, ferroelectric, flash) has emerged as a clear winner, betting on one too early carries real obsolescence risk, making heterogeneous analog-digital designs the more pragmatic current path.
References
- IBM Research — “How the von Neumann bottleneck is impeding AI computing”
- Towards Data Science — “Analog AI Is Back, But Can It Survive Its Own Noise?”
- PatSnap — “In-memory analog computing landscape 2026”
- ScienceDirect — “A review on selective in-memory computing processors”
Related reading on The Tech Trends: HBM Memory Bandwidth, Photonic Computing for AI, Thermodynamic and Probabilistic Computing Chips, and Quantization Explained.
