ANNIE — Algorithmic Neural Network Inference Engine — is a software layer that reduces AI inference compute energy by 60–80% without hardware changes. We are opening a joint research programme with the NUS ECE SEEDER Group targeting ACML 2026 and Singapore's SS 715:2025 green data centre standard.
Singapore's SS 715:2025 Data Centre Standard mandates energy efficiency but defines no measurement framework for the IT layer — the algorithms running inside the hardware. IMDA and EDB cannot currently compare or validate competing efficiency claims from data centre operators. A joint ANNIE Systems–NUS ECE SEEDER Group paper fills that gap.
Singapore's green data centre roadmap requires measurable energy efficiency improvements. The PUE metric captures infrastructure efficiency but ignores algorithmic efficiency entirely.
No peer-reviewed benchmark methodology currently exists for evaluating inference efficiency at the algorithm level. Vendors self-report. Regulators cannot verify.
A joint NUS ECE SEEDER Group–ANNIE Systems paper proposing and validating a benchmark methodology would position Singapore as the origin of the global inference efficiency standard.
ANNIE is a software layer that wraps existing transformer models and replaces computationally expensive operations with mathematically equivalent alternatives. The techniques are independently validated and compound in their efficiency effect.
Replaces floating-point multiply-accumulate operations with integer bit-shift arithmetic. Eliminates the primary source of GPU energy consumption in weight-matrix computation.
Replaces the exponential function in softmax attention with an integer approximation. Removes transcendental function overhead from the attention mechanism.
Replaces the GELU activation function — which requires erf() evaluation — with a piecewise-linear approximation using only integer slopes and thresholds.
Routes attention computation only through non-negligible attention scores. Reduces attention complexity from O(n²) to O(nnz) for sparse attention patterns.
ANNIE wraps any HuggingFace-compatible transformer model in a single function call. No hardware procurement, no infrastructure changes, no retraining. The efficiency gains are immediately measurable on existing H100, A100, or any CUDA-capable GPU — making H100 benchmark data on AWS Singapore immediately actionable for SS 715:2025 compliance reporting.
We are proposing a two-phase research collaboration with Asst. Prof. Kelvin Fong's SEEDER Group (Semiconductor & Emerging Electronic Devices Exploratory Research) at NUS ECE. Phase 1 targets ACML 2026. Phase 2 extends into hardware-algorithm co-design for the Digacus-Nano silicon programme.
Joint ANNIE Systems–SEEDER Group benchmark paper establishing a peer-reviewed methodology for measuring algorithmic inference efficiency on H100 infrastructure in AWS Singapore (ap-southeast-1). SEEDER researchers named as co-authors.
We are applying for AWS Research Credits with Asst. Prof. Kelvin Fong (SEEDER Group) as co-PI. All H100 compute costs on AWS Singapore (ap-southeast-1) are covered by the credits — zero cost to NUS ECE or the SEEDER group.
Extending into Digacus-Nano ASIC co-design on GlobalFoundries 22FDX (Fab 3E, Woodlands, Singapore) — a custom integer compute array purpose-built for ANNIE inference. SEEDER's device/circuit/algorithm co-design expertise maps directly to this phase. Joint NRF/EDB IAF grant application.
ANNIE Systems retains background IP. Jointly developed research results are co-owned with NUS under a standard university-industry IP agreement. SEEDER Group researcher co-authorship on all publications.
Working implementation of all four ANNIE techniques on GPT-2 and LLaMA-3 architectures. Benchmark framework measuring tokens/sec, active watts, and per-technique compute reduction. Full technical documentation available under NDA.
A named SEEDER Group / NUS ECE co-PI for the AWS Research Credits application. Hardware measurement methodology expertise. SEEDER graduate researcher involvement in the benchmark design and paper authorship.
ANNIE Systems is a Singapore-incorporated deep-tech company founded at the intersection of algorithmic research and sovereign AI infrastructure.
Directed to the NUS ECE SEEDER Group (Asst. Prof. Kelvin Fong). We also welcome enquiries from NTU, SUTD, and A*STAR research groups working on energy-efficient computing and hardware-algorithm co-design. Technical documentation available under NDA.
arnold@annie.systems