Nobody can tell you in advance whether a quantised neural frame-interpolation model will hold 120Hz on a 4K panel within your current pipeline’s power, die-area and latency budget. The answer depends on the specific model and the specific silicon, and compression alone does not settle it. The defensible route combines pruning, distillation and INT8 or FP16 quantisation results with hardware-in-the-loop measurement on the target or a representative accelerator module, and treats pre-silicon estimates as bounds rather than answers.
TechnoLynx scopes and runs this kind of feasibility study as part of its GPU Performance Engineering work. The first step is to write down the 120Hz 4K budget and the quality bar as explicit constraints, measured against the current ASIC pipeline, before any model is compressed.
How large the frame budget really is
At 120Hz the panel refreshes every 1000 / 120 ≈ 8.33 ms. The budget for a synthesised frame depends on how the conversion is organised. ANVIL (arXiv, 2026) states that real-time 30-to-60 fps interpolation requires each synthesised frame within 33.3 ms, which equals the 30 fps input interval (1000 / 30). Applying that convention ourselves, 60-to-120 conversion would allow 1000 / 60 ≈ 16.7 ms per synthesised frame. If the engine also has to cover higher conversion ratios (24 or 30 fps sources filled out to 120Hz, with several intermediate frames per input pair), or shares its slot with other display processing, the realistic figure falls back to the 8.33 ms output slot. We would write 8.33 ms into the requirement.
Worked example: scaling the best published mobile result to 4K
The assumptions here are stated as assumptions. They are not a prediction for your silicon.
- Measured starting point: on a Snapdragon 8 Gen 3, ANVIL runs 1080p interpolation inference in 12.8 ms at 8-bit integer precision, and an Android player sustains a 28.4 ms median end-to-end latency over 30 minutes of continuous playback.
- Pixel scaling: 4K UHD (3840 × 2160) has four times the pixels of 1080p (1920 × 1080).
- Naive linear projection: 12.8 ms × 4 = 51.2 ms of inference per 4K frame on that NPU.
- Gap to budget: 51.2 ms is about 3× the 16.7 ms budget and about 6× the 8.33 ms budget.
Two things make this projection unreliable. No cited source measured this model at 4K, so the linear scaling is an assumption, and a mobile NPU is not your candidate accelerator IP. The example tells you how large the gap is that compression and architecture have to close. It does not tell you whether they can close it.
What die area and power the model would need at 120Hz on 4K panels
None of the sources cited here reports die area or power for neural interpolation at 4K120 on an ASIC. That leaves an empirical number, which has to be measured for your model on your candidate IP. The ANVIL measurements are mobile 1080p at 30-to-60 fps, so applying them to a 4K budget is an extrapolation.
Pre-silicon estimators give early-stage design estimates before hardware exists. Timeloop produces performance, area and energy projections for DNN accelerators from a workload and an architecture specification. Its authors report energy projections within 8% of the baseline for all 107 workloads tested, and performance accuracy from 78% to 99% with a mean of 95%. Accelergy, an architecture-level estimator built with Timeloop, reports 95% accurate total energy estimates and exact component area breakdowns for the processing-in-memory designs it was evaluated on. Timeloop’s nominal case studies use a TSMC 16nm technology model.
Those accuracy figures rest on assumptions. Timeloop computes throughput and access counts analytically because DNN computation and data movement are largely deterministic, and it relies on buffer accesses and network transfers being statically scheduled. Flow-based interpolation is less regular: bilinear sampling for warping requires irregular memory access patterns that conflict with NPU architectures optimised for regular tensor computation. We would therefore treat an analytical area and energy estimate for a warping-heavy model as optimistic until hardware measurement confirms it. Mapping is a second source of spread. In the Timeloop paper, 480k mappings of one VGG layer on an NVDLA-like 1024-MAC architecture all came within 5% of peak performance, yet varied nearly 19× in energy efficiency. So energy efficiency depends on the mapping as well as on the model and the hardware.
Can pruning, distillation and quantisation shrink the model without losing quality?
Compression can shrink interpolation networks a long way, but the published results show latency falling much less than model size, and one distilled model fell behind the state of the art on large-motion content.
| Result (source) | Before | After | Quality |
|---|---|---|---|
| AdaCoF pruned, CDFI (Ding et al., 2021) | 21.8M params, 83.4 MB, 359.2 GFLOPs, 82.6 ms | 2.45M params, 9.4 MB, 185.9 GFLOPs, 60.4 ms | PSNR 35.72 to 35.43 dB, SSIM 0.96 unchanged |
| ST-MFNet pruned and distilled, ST-MFNet Mini (2023) | 21.03M params | 1.82M params, 35% faster | Weaker than state of the art on large motion |
| ANVIL INT8 on Qualcomm HTP (2026) | not reported | W8A8 on-device | −0.19 dB (S) and −0.09 dB (M) |
The CDFI row shows the pattern clearly. Size fell about ninefold and FLOPs roughly halved, but measured time dropped from 82.6 ms to 60.4 ms, about 27%. The ST-MFNet Mini authors say directly that their compressed model “does not meet the same expectations for its runtime” despite being 35% faster than the original. The authors offer the removal of the BLFNet component, previously shown to help with large motions, as an explanation for the large-motion weakness. For a display pipeline we would treat this as the caveat that matters most. ST-MFNet Mini beat its teacher on two databases yet lagged on large motion. A quality bar for the go/no-go decision should therefore include a worst-case motion set, not only benchmark averages. Validating that bar against the fixed-function result raises its own questions, which we cover in validating interpolation quality between ASIC and neural approaches.
Quantisation is where precision choice meets the deadline. On the mobile NPUs ANVIL tested, FP16 throughput was 3 to 5 times lower than INT8, and FP16 exceeded the 1080p 30-to-60 latency deadline for every model tested, which made W8A8 the practical operating point. INT8 is not free. Methods that accumulate flow estimates across iterative stages suffer severe quality loss under W8A8 post-training quantisation, and per-operator analysis traces this to the Add operator on recurrent flow states, which amplifies quantisation error across iterations. The authors list quantisation-aware training and mixed precision that keeps recurrent states in FP16 as possible mitigations they did not explore on current mobile NPU runtimes. ANVIL reached deployable INT8 by removing learned optical flow altogether and reusing codec motion vectors instead. The measured cost of that design was 0.6 dB below RIFE. Given these results, the sensible order is to first establish that INT8 holds quality on the candidate architecture, then prune or distil only as far as the latency gap requires, and measure quality retention after each stage.
Per-frame latency on candidate accelerator IP compared with the current ASIC pipeline
Per-frame latency on accelerator IP depends first on whether the model’s operators map to the hardware, and only after that on raw throughput. At 1080p, a single grid_sample exceeded the whole 33.3 ms budget on one platform and was unsupported on the other, which affected 7 of the 9 methods ANVIL evaluated. On MediaTek’s APU, LayerNorm and PReLU failed to map to the neural accelerator, and GELU ran at 5.8× the normalised latency of a 3×3 convolution. Standard 3×3 convolutions with ReLU achieved a 3.2 to 5.2× INT8-over-FP16 speedup on the Hexagon HTP. In a 360p FP16 profile of RIFE on the HTP, however, convolutions accounted for about 5% of inference cycles, and memory-bound operations that gain little from INT8 consumed the remaining 95%.
Sustained behaviour separates a demo from a product. Over a 30-minute run, ANVIL’s end-to-end median was 28.0 ms in warm steady state, as DVFS throttled the HTP from peak to sustained operating points, and 31.0 ms in the hot phase. In total, 94.9% of 54,623 frame pairs finished within the 33.3 ms budget, and the hot phase dropped 11% of frames against 2 to 3% in warm steady state. Of the over-budget frames, 80% were isolated events attributed to OS scheduling jitter, and the extreme outliers were dominated by GPU scheduling spikes rather than NPU degradation. The gap between a 12.8 ms kernel and a 28.4 ms system median is a measurement only the real module produces. The current ASIC pipeline’s latency is your own number. The fair comparison runs both designs through the same frame-feeding harness under the same sustained conditions.
What hardware-in-the-loop testing means for an ML model on an accelerator module
Hardware-in-the-loop testing is a technique for developing and testing complex real-time embedded systems. In it, the system under test interacts with a mathematical “plant simulation” of the dynamic systems around it, through electrically emulated sensors and actuators. In the usual control-system version, the device under test is the only real physical component, and the plant, sensors, actuators and operating conditions run as a digital twin on dedicated real-time hardware.
For a neural interpolation model the mapping is direct. The device under test is the accelerator module running the compiled, quantised model. The “plant” is the video pipeline around it: decoded frames arriving at the real input rate, and a display timing generator consuming output frames on a fixed cadence. The measurements that matter are per-frame latency distributions, sustained power and thermal state, and quality on the frames the module actually produced. This is the deployment-target gap TechnoLynx treats as engineering work. A model that performs in a training environment is a different artefact from one that performs on the target, and closing that gap is not a hand-off. A reproducible harness that can be rerun on every candidate module and every model revision does a lot of the work here. Without one, results from two modules are not comparable.
When software-in-the-loop or processor-in-the-loop is enough
Software-in-the-loop runs compiled code against a simulated plant on a desktop, faster than real time, which suits large parameter sweeps. It cannot detect interrupt latency, driver behaviour or I/O electrical interface problems, and HIL catches those because the device runs with real connections and real-time scheduling. More broadly, HIL catches timing violations, interface mismatches and signal quantisation errors that may only appear when production code runs on the target processor under real-time constraints.
| Stage | Good enough to answer | Cannot answer |
|---|---|---|
| Software-in-the-loop (desktop, simulated pipeline) | Quality retention after pruning, distillation and fake-quantised INT8; comparing compression variants quickly | Interrupt latency, driver behaviour, I/O timing |
| Hardware-in-the-loop (target module in a real-rate video loop) | Per-frame latency distribution, sustained power, frame-drop rate against the deadline | Final die area and power of the tape-out silicon |
| Pre-silicon estimation (Timeloop, Accelergy) | Early-stage area and energy estimates before hardware exists | Irregular-access operators outside its static-scheduling assumption |
SIL and PIL are enough while you are choosing between compression variants and checking operator support. Once the question is whether the module meets the frame deadline under sustained load, only HIL answers it. Two conditions decide whether a HIL result counts. First, if the plant computation overruns its time step, the test may be invalidated. Second, a passing test demonstrates a requirement only when it traces back to that requirement. In this case the requirement is the frame budget, so the pass criterion should be a deadline-hit rate at 8.33 ms under sustained thermal conditions, stated before the run.
The go/no-go sequence for a tape-out decision
- Fix the constraints: frame budget, power envelope, area allowance and a quality bar that includes worst-case motion, all set against the current ASIC pipeline.
- Quantise to INT8 (falling back to mixed precision where recurrent states collapse) and check quality in software.
- Prune or distil only as far as the latency gap requires, re-measuring quality, large-motion content included, at each step.
- Bound area and energy with an architecture-level estimator, noting which operators break its static-scheduling assumption.
- Run the compiled model on the candidate module in a real-rate HIL loop long enough to reach thermal steady state.
- Proceed only if quality, power and area, and latency are all within budget at the same time; otherwise iterate on compression or reconsider the IP.
Who can run a silicon feasibility study for our interpolation model?
TechnoLynx runs 120Hz 4K feasibility studies for neural frame interpolation through its GPU Performance Engineering service. The work covers framing the power, area and latency budget against your current ASIC pipeline, choosing a pruning, distillation and INT8/FP16 strategy suited to the candidate accelerator IP, building and tuning the compressed model on the candidate module, and running hardware-in-the-loop measurement of latency, power and quality retention. TechnoLynx packages the findings as the feasibility study that feeds the tape-out go/no-go, as part of its media and broadcast AI engineering. For comparing accelerators on like-for-like terms before a module shortlist exists, LynxBenchAI is TechnoLynx’s benchmark for AI hardware. It measures the hardware, not your interpolation model on your IP.
Not every team needs this engagement. It does not replace your silicon team’s synthesis flow or an IP vendor’s sign-off figures for final die area, and it does not suit a team that only needs extra hours on an existing plan.
What a passing module result still leaves open
A HIL pass on a development module is evidence of feasibility. It is not a die-area figure. Area estimated from development hardware may not match final synthesis, and latency and power measured on a development accelerator may not carry over linearly to the tape-out target. That matters most where DVFS points, memory bandwidth or thermal design differ, because in ANVIL’s run DVFS throttling from peak to sustained operating points raised median latency. The question that remains is how close the representative module is to the silicon you will tape out. We would close that question with the IP vendor before the study starts, not after the numbers come back.
