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AI Runtimes

SPOT-Tuned AI Runtime

AI Runtime kv mobile

SPOT-tuned hardware–software synergy for ultra-low power AI on Apollo. Choose heliaRT™ for drop-in, backward-compatible speedups; choose heliaAOT™ for compile-time
optimization and the smallest, most power-efficient builds.

AI Runtimes Highlights

01

SPOT-Tuned Synergy

Co-designed with Apollo silicon. Kernel optimizations, memory planning, and dataflow tuned to SPOT so models run efficiently on real devices—not just in benchmarks.

02

Drop-In or Dialed-In

Choose heliaRT for TFLM-compatible, drop-in speedups—or heliaAOT for compile-time control with dials for size, speed, and power.

03

Smaller Footprint, Faster Starts

heliaAOT emits only what your model needs; heliaRT trims overhead vs baseline TFLM. Result: lean binaries, quick init, more room for features.

04

Performance per Microamp

SPOT-tuned runtimes squeeze latency and memory while minimizing energy draw, so real-time AI runs comfortably on battery—perfect for wearables, wellness, and smart audio.

AI Runtime Comparisons

Aspect
TFLM
heliaRT(Optimized TFLM)
heliaAOT(Ahead-of-Time)
Primary Fit
Portable baseline
micro-inference
Drop-in, Apollo-tuned
upgrade
Max efficiency & control for production
Performance on
Apollo
Good baseline
Faster via SPOT-tuned
kernels & planning
Fastest via compile-time
specialization/fusion
Memory Footprint
Moderate interpreter +ops
Leaner than TFLM
Smallest—emits only
what the model needs
Deployment &
Updates
Load .tflite at runtime;
very easy swaps
Same .tflite flow;
backward-compatible
with TFLM
Compiled artifact
(C/obj/bin); update
requires rebuild
Maturity / “Battle-
Tested”
Highest (widely used)
High (production-
hardened on Apollo)
Newer (rapidly maturing)
Op & dtype
coverage
Broad ops;
int8/int16/int32
Broad ops;
int8/int16/int32 (TFLM-compatible)
Focused ops;
int8/int16 (no int32)
Optimization
Control
Limited runtime knobs
Apollo-aware planners
& tuned kernels
Most control:
size/speed/power,
layout/schedule
Best For
Rapid prototyping & portability
Products with frequent
model refreshes
Locked-down SKUs
with tight latency,
memory, energy targets

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