I've been discussing Huawei's τ scaling (temporal scaling) with people recently, and noticed the conversation tends to stay at the surface level without reaching its substance — likely because many participants don't come from an EE background and aren't familiar with the classical meaning of τ in circuit theory. The very first time constant you learn in a circuits course is τ = RC: the resistance of a wire multiplied by its capacitance gives the order of magnitude of the time a signal needs to traverse that wire. The longer the wire, the greater the resistance and capacitance, and the slower the signal. Within this framework, the past sixty years of geometric scaling are reinterpreted as one particular implementation of temporal scaling. Transistors were shrunk to shorten switching delay; circuits were packed more tightly to shorten metal interconnects and reduce signal propagation delay. Geometric scaling was only ever the means — compressing delay was always the end. Huawei's thesis is that once geometric scaling stalls, you find other ways to keep compressing delay.
As it happens, He Tingbo's τ scaling paper released its v2 a couple of days ago, expanding from 16 to 23 pages. I compared the two versions: the data and conclusions are unchanged. The additions are essentially responses to several points of criticism the industry raised about v1. Three are worth discussing.
The most important addition is the test evidence now backing the previously bare claim of "41% energy efficiency improvement." In v1, that number had no baseline and no test conditions — the most obvious target for scrutiny. V2 supplies a full comparison table. The baseline is the 2025 Kirin 9030 Pro. Both chips use the same mature process node; the key difference is that the baseline uses a conventional planar design, while Kirin 2026 folds critical paths across two vertically bonded wafers. Folding shortens interconnects and reduces interconnect delay. The timing margin freed up on the critical path translates directly into a higher maximum clock frequency: 3.1 GHz at 1.1 V supply, 13% above the baseline. The "41% energy efficiency improvement" comes from a separate operating point specifically configured for an iso-performance comparison: voltage scaled down to 0.9 V, frequency scaled down to 2.5 GHz, with measured power at 25°C coming in at 0.59× the baseline. A back-of-the-envelope estimate checks out: dynamic power scales roughly with the square of supply voltage, so an 18% voltage reduction contributes about one-third of the power drop from the square term alone. Factor in the 9% frequency reduction and the interconnect capacitance eliminated by folding, and you land right around 0.59×. So the precise meaning of "41% energy efficiency improvement" is power reduction at iso-performance. In essence, the timing margin gained from folding is traded for lower power consumption; the efficiency gain comes from logic folding. As a side note, v2 also reports that power density after dual-layer stacking is actually 5.6% lower than the baseline.
The second addition addresses the question peers are most likely to ask: 3D stacking has been around for years — AMD's 3D V-Cache and Intel's Foveros are both in volume production — so what's new about LogicFolding? To understand the paper's answer, you first need to know how two layers of silicon communicate. They rely on inter-layer bond pads, which function like elevators connecting the upper and lower floors. In prior production 3D stacking, bond pad pitch ranges from 9 μm to tens of micrometers, yielding roughly ten thousand connections per square millimeter — enough to attach a bus to an entire cache block. So the established design approach has been to move complete functional blocks wholesale onto the upper tier. AMD, for example, stacks an entire cache die on top of a processor die; the two tiers are designed independently and connected through an interface. But inside a chip, a single square millimeter contains hundreds of millions of transistors. If you want adjacent logic gates to sit on different tiers — one on top, one on the bottom — that connection density falls far short. Kirin 2026 brings bond pad pitch down to 1.5 μm, yielding 440,000 connections per square millimeter. That approaches the density of the top-level metal wiring inside a chip. Routing a signal across tiers costs roughly the same as routing it across metal layers within a single die. At this point, the two silicon layers merge into a single entity in the circuit sense. EDA tools can decide at the individual logic-gate level which gate goes on which tier, handing the problem to algorithms for global optimization — a completely different degree of design freedom from what came before. The paper also explains why they didn't take the more aggressive route of fabricating a second device layer directly on top of the first. That approach offers the finest inter-layer connectivity, but manufacturing the second layer requires high temperatures that damage the already-completed first layer. It isn't production-viable today.
The third addition is thermal management. Vertical stacking significantly increases thermal density per unit area, and the lower die's heat dissipation path is blocked by the upper die. This is the first objection anyone raises about 3D stacking, and v1 did not address it in depth. V2 openly acknowledges that thermal management remains a key challenge for the LogicFolding architecture. The countermeasure is thermally-aware partitioning and floorplanning: during the design phase, high-power circuits are excluded from folding candidates, and the floorplan avoids placing high-power blocks in vertical adjacency to prevent hotspot superposition. Whether this strategy is a set of manually imposed engineering constraints or has already been codified into an automated flow within their internal EDA tools, the paper does not say. It only identifies a multi-physics tool chain as the single most important investment for the next decade. Combined with the measured data showing power density 5.6% below the baseline at the iso-performance operating point, the thermal concern has at least received a direct response. That said, this approach is fundamentally avoidance-based. As stacking grows to three or four tiers, the design space eligible for folding will be progressively squeezed by thermal constraints — a boundary the paper does not explore.
Additionally, v2 includes a cross-sectional micrograph of the bond interface between the two wafers and explicitly states that wafer-on-wafer hybrid bonding is used. This spec is worth benchmarking against the industry: 1.5 μm pitch wafer-to-wafer hybrid bonding on a production logic chip has no precedent. TSMC's SoIC is currently in production at 6 μm pitch; Intel's Foveros Direct is at 9 μm. Impressive, to say the least.
After comparing the two versions, I'm left with two questions. One is about equipment: who supplied the bonding tools capable of this spec? The paper says only that it is the result of years of process development across a multi-vendor ecosystem. The other is about EDA: designing two wafers as a single chip is beyond what any commercially available EDA tool can do today. The paper acknowledges this, stating only that methodological details will be "published within months." Yet the frequency table shows that the 2027-generation Kirin at 3.39 GHz is already tagged as having physical silicon, meaning this toolchain was up and running inside Huawei long ago — and has been validated on at least two product generations. My personal guess is that this EDA capability was built in-house by Huawei. If anyone has insight on this, I'd welcome the discussion.
