The Quadric Chimera Software Development Toolkit (SDK) is a comprehensive environment for the development of complex application code targeting the Chimera processor. The Quadric SDK enables the mixing and matching of any data parallel algorithm whether it is expressed as machine learning / AI graph or as traditional C++ code.
Preparing an AI model to run on a Chimera core is simple and tool-driven • No special network preparation • No substitution of operators • No removal of layers • No reliance on Quadric to prepare models
Your Private Cloud or On Premise
Quadric users can download a complete Docker image of the Chimera SDK for deployment on private clouds or on-premise systems. Once installed on your compute resources, you can compile ONNX graphs into C++ using the Chimera Graph Compiler, compile your own proprietary C++ code, combine them and profile on the cycle accurate ISS model of the Chimera GPNPU.
The Quadric Chimera Graph Compiler (CGC) is a powerful graph conversion and code optimization tool that inputs a machine learning inference model created from the leading AI training frameworks, performs numerous optimizations, and outputs an optimized C++ code representation of the AI graph utilizing the Chimera Compute Library (CCL) for later compilation by the Chimera LLVM C++ compiler.
Graph Import CGC accepts input graphs in ONNX format for networks developed in PyTorch, TensorFlow or other frameworks. A number of optimizations are performed as part of the graph import phase: • Graph simplification / canonicalization • Constant propagation - removing operators with purely constant arguments, if possible • Operator legalization / conversion – converting to GPNPU-specific forms of AI operators
The shape and structure of the network graph is optimized to simplify operators where possible. Compatibility checks are performed to determine if all operators are supported. When an unsupported or custom operator is present in the source graph, the user has the option to partition the graph around the custom operator, write a C++ kernel for the unsupported operator, and reintegrate the new custom operator C++ kernel with code auto-generated by CGC.
Graph and Memory Optimization CGC creates a full intermediate representation of the input graph and performs multiple passes of optimization with the twin aims of optimizing performance and memory bandwidth utilization.
Memory optimization techniques include: • Tensor format layout analysis (both L2 and LRM) • Fusion in Local Memory (FILM) - merging of operations to preserve data within LRM and avoid costly intermediate activation writeback. • Array-level memory adapters & tensor shape transformations • Bank conflict minimization, defragmentation • Predictive weight prefetching
Data Normlized to 7nm SSGP @1GHz fmax: 1660 FPS
7nm SSGP Average Power by FILM Region: 1803 mW
External Bandwidth by FILM Region
Profile by FILM Region
1,660.4 Normalized FPS @1GHz; 602263 Cycles
Chimera LLVM C++ Compiler & Instruction Set Simulator
The Chimera LLVM C++ compiler utilizes the industry state of the art LLVM compiler infrastructure with a Quadric-specific code generation back end that emits assembly code specific to the Chimera instruction set.
The Chimera Instruction Set Simulator (ISS) is a pipeline-accurate executable model of the Chimera processor core. The ISS can be utilized both in a standalone mode to profile and tune application code in isolation, or as a callable System C transaction-level model bundled into a more comprehensive virtual prototype of an SoC where more accurate memory model behavior can be used to fine-tune your Chimera code more precisely.
The Chimera ISS provides extensive profiling of cycle counts, data bandwidth usage, and power profiles of each unique application workload.
