Rethinking AI Compute
Toward Efficient, Low-Cost and General-Purpose Intelligence at the Edge
Investigating new neural architectures, memory systems, sparse computation, and adaptive inference for capable AI under severe compute, energy, memory, and connectivity constraints.
The Core Research Problem
Modern AI is Brilliantly Expensive
Large models require substantial GPU/accelerator compute, memory, energy, bandwidth, storage, cooling, and inference infrastructure. This creates a fundamental economic question:
“If the cost of computation required to produce intelligence approaches or exceeds the economic value generated by that intelligence, can AI achieve truly massive adoption?”
Rather than simply making existing models smaller, we want to investigate whether the computational architecture of AI itself can be redesigned.
GPU / Accelerator Compute
Memory & Storage
Energy & Cooling
Bandwidth & Infrastructure
Can we develop a fundamentally more compute-efficient architecture for AI that achieves useful perception, reasoning, memory, and decision-making on low-cost edge hardware?
This is our North Star question.
Research Directions
Eight Interconnected
Research Areas
Rethinking the Transformer
- RQ1
Which Transformer mechanisms are essential for intelligence vs. disproportionately costly?
- RQ2
Can attention be replaced or selectively activated without degrading reasoning?
- RQ3
Can recurrent, state-space, sparse, or hybrid architectures offer a more efficient alternative?
- RQ4
Does persistent internal state require less compute than processing full contextual sequences each time?
Persistent Intelligence
- RQ5
Can persistent world state replace repeated recomputation of previously understood information?
- RQ6
Can an AI maintain a compact internal representation and update only what changes?
- RQ7
What is the optimal persistent state representation? (latent, symbolic, neural, semantic graph...)
Event-Driven Intelligence
- RQ8
Can event-driven computation substantially reduce AI's continuous compute requirements?
- RQ9
How can an AI judge whether incoming information justifies additional computation?
- RQ10
Can an AI dynamically scale computational complexity to task difficulty?
Intelligence per Watt
- RQ11
How should AI intelligence efficiency be measured? IE = Performance / Compute | Energy | Cost?
Edge Intelligence
- RQ12
What capability level can be achieved under strict compute, memory, energy, and bandwidth constraints?
- RQ13
Can a general-purpose AI operate effectively without continuous cloud connectivity?
- RQ14
What architectural changes are required when designing for edge hardware first?
Multimodal Intelligence
- RQ15
Can multimodal information be unified through a shared persistent world state?
- RQ16
How can vision, language, spatial reasoning, and sensor data be efficiently fused on edge devices?
Embodied Intelligence
- RQ17
Can compute-efficient architecture support continuous perception, reasoning, planning, and action in unstructured environments?
- RQ18
How should an AI represent objects, space, uncertainty, goals, and temporal changes for physical interaction?
- RQ19
Can an edge AI learn from its environment without continuously uploading data to the cloud?
The Bigger Question
“Do we need larger models to obtain more intelligence, or do we need better architectures that use computation more intelligently?”
That's our strongest research question.
Primary Hypothesis
Falsifiable & Testable
AI systems that combine persistent state, selective computation, adaptive inference, efficient neural architectures, and multimodal world representations can achieve competitive real-world task performance while requiring substantially less computation, memory, energy, and connectivity than conventional continuously executing large-model architectures.
Research Objectives
Eight Clear
Objectives
Each objective is concrete and measurable. We aren't chasing vague milestones — we're building science.
Investigate the computational inefficiencies of current AI architectures.
Identify alternative mechanisms for attention, memory, reasoning, and representation.
Develop experimental architectures optimized for edge deployment.
Develop methods for adaptive and event-driven computation.
Develop a persistent multimodal world representation.
Benchmark capability against compute, memory, latency, energy, and monetary cost.
Deploy the resulting architecture on real robotic systems.
Release research findings, benchmarks, datasets, models, and tools openly where practical.
Proposed Architecture
A New Intelligence Pipeline
Current Paradigm
Stateless · Expensive · Repetitive
TVA Proposed Architecture
Persistent · Efficient · Adaptive
The Experimental Question
Can we match intelligence with
10× less compute?
Then achieve it with
100× less?
Ultimately: What is the
minimum computational substrate for useful general-purpose AI?
Edge Intelligence
Efficiency Benchmark
EIEB
A rigorous, multi-dimensional benchmark measuring AI capability where it really counts: the edge.
Intelligence Efficiency Metrics
Intelligence Per Watt
& Per Dollar
Compute Efficiency
IE = Performance / Compute
Task Performance per FLOP
Energy Efficiency
IE_E = Performance / Energy
Useful Intelligence per Watt
Economic Efficiency
IE_C = Performance / Cost
Useful Intelligence per Dollar
The Ultimate Vision
Intelligence
Everywhere
TVR A1 is our embodied research platform. But the bigger vision extends far beyond any single robot.
Robots
TVR A1
Devices
Edge AI
Machines
Industrial AI
Not merely for wealthy companies with enormous GPU clusters, but for robots, farms, factories, vehicles, devices, schools, and communities everywhere.
Research Philosophy
We are not trying to make AI bigger.
We are trying to make intelligence cheaper.
Today's AI paradigm has demonstrated what massive computation can accomplish. Our question is what can be accomplished when computation is scarce. We believe the next major leap in AI may not come solely from larger models, but from fundamentally more efficient ways of representing, processing, remembering, and reasoning about information.
TVA Robotics Research Identity:
“We are researching the economics and architecture of efficient machine intelligence.”
Who We Want to Join
Open to
All Minds
Students and independent researchers can contribute through literature reviews, experiments, benchmarking, simulations, datasets, model optimization, theoretical work, hardware experiments, documentation, and more.
You do not need to be an expert.
Curiosity, rigour, and commitment to open science are the only prerequisites.