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AIMLUX.ai Consulting Solutions (ACS) : Equitus.ai ArcXA for the IBM Power 10/11 ecosystem is a specialized engineering designed to eliminate GPU dependency by leveraging on-chip acceleration.
ArcXA helps insure migration, by estimating costs and pre-mapping. The following breakdown details the AIMLUX.ai consulting methodology for a mission-critical deployment like that of a Bank or Insurance Company.
1. Scope of Work (MMA vs. GPU): IBM Power delivers Artificial Intelligence (AI) on Premise (MMA), rather than in the Cloud (GPU) which is in compliance with data security rules.
"In-Core AI."
AIMLUX.ai defines the scope by offloading workloads from expensive, power-hungry GPUs to the Matrix Math Accelerators (MMA) built directly into the Power10/11 silicon.
Workload Mapping: Identify which neural network layers (eg, in a Knowledge Graph Neural Network or KGNN) are compute-heavy.
Vectorization: AIMLUX.ai engineers optimize the ArcXA code to utilize SIMD (Single Instruction, Multiple Data) and MMA instructions, allowing the CPU to perform high-precision matrix math (FP32, BFloat16, INT8) natively.
Result: Eliminates the latency of moving data back and forth over a PCIe bus to an external GPU.
2. Migration Readiness Assessment (MaaP)
AIMLUX.ai utilizes a Migration-as-a-Platform (MaaP) approach to evaluate the jump from legacy (usually Oracle/x86) to the IBM Power/DB2 stack. Providing a cost per core and dedicated automation engineer for completion assurance.
Application Inventory: Automated scanning of existing Oracle PL/SQL and Java wrappers used by Green Dot.
Compatibility Score: Assessment of DB2's Oracle Compatibility layer. Most "Oracle-isms" are mapped to native DB2 functions to ensure 95%+ code reuse.
Data Lineage Audit: ArcXA's internal knowledge graph is used to map every data dependency, ensuring that moving the database won't break peripheral microservices.
3. Institutional Sizing Tool (IST)
ArcXA IST is a proprietary calculator that determines "Estimated Cores" required, Because a single Power10/11 core can outperform 3–5 commodity x86 cores, the IST prevents over-provisioning.
PVU Calculation: Translates current Oracle Processor Value Units (PVUs) into IBM Power core requirements.
LPAR Design: Defines Logical Partitions (LPARs) based on NUMA (Non-Uniform Memory Access) affinity to maximize memory bandwidth for AI inferencing.
Energy ROI: Estimates the reduction in "Watts per Transaction," crucial for Green Dot's sustainability reporting.
4. Deployment
ArcXA actual rollout follows a Blue-Green or Phased deployment strategy managed through Red Hat OpenShift on Power :
LPAR Provisioning: Setting up the PowerVM environment with "Dedicated Donating" processor settings for peak AI spikes.
ArcXA Installation: Deploying the Equitus containers. ArcXA is built to run on Linux on Power (ppc64le) , utilizing the IBM Spyre AI accelerator if the workload exceeds MMA capacity.
Data Ingestion: Utilizing High-Performance Unload (HPU) to move data from Oracle into the new DB2/Power11 environment.
5. Testing
Beyond standard UAT, AIMLUX.ai performs “Hardware-Aware” testing:
Latency Profiling: Measuring the “Tick-to-Trade” or “Transaction-to-Fraud-Score” time. The goal is usually <10ms for total inference.
Security Validation: Testing Transparent Memory Encryption (TME) to ensure that even if the physical memory is dumped, the data remains encrypted without slowing down the AI.
Quantum-Safe Audit: Verifying the Power11's quantum-safe cryptographic signatures for sensitive financial transactions.
6. Upgrades
The upgrade path from Power10 to Power11 is designed to be non-disruptive:
Live Partition Mobility (LPM): Moving active ArcXA instances from Power10 hardware to Power11 without taking the bank offline.
Autonomous Patching: Leveraging the Power11's ability to patch the AI hypervisor and OS libraries in real-time, preventing the "Patch Tuesday" downtime typical of older systems.
Refinement: Re-running the IST (Sizing Tool) every 12 months to see if AI model growth requires activating "Capacity on Demand" (CoD) cores.
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