but as a reachable threshold. If / when achieved, AGI would redefine what software (and related hardware) can do. Rather than executing pre-programmed tasks, AGI systems would understand goals, generate storage, but for real-time inference and model training workloads that require dense, high-power hardware. As AI moves from experimental to essential, so too do data centers. Per NVIDIA Co-Founder and the same time, the cost of applying/using these models – known as inference – is falling quickly. Hardware is improving – for example, NVIDIA’s 2024 Blackwell GPU consumes 105,000x less energy per token

ChenCurrent Deep Learning Landscape Frameworks and Inference engines DL Compilers Kenrel Libraries Hardware CuDNN NNPack MKL-DNN Hand optimized Open source, automated end-to- end optimization framework Optimization AutoTVM Device FleetExisting Deep Learning Frameworks High-level data flow graph Hardware Primitive Tensor operators such as Conv2D eg. cuDNN Offload to heavily optimized DNN operator graph and optimizations Directly generate optimized program for new operator workloads and hardware Hardware FrameworksWhy Automation is the Future Clear winner on emerging models in product Competitive

INTERNATIONAL INDUSTRIES, INCORPORATED 8 Hardware Configure Chisel VTA for DE10-Nano DEPLOY VTA ON INTEL FPGA©2019 HARMAN INTERNATIONAL INDUSTRIES, INCORPORATED 9 Hardware Datapath of Chisel VTA DEPLOY VTA VTA ON INTEL FPGA©2019 HARMAN INTERNATIONAL INDUSTRIES, INCORPORATED 10 Hardware DEPLOY VTA ON INTEL FPGA©2019 HARMAN INTERNATIONAL INDUSTRIES, INCORPORATED 11 Getting Started DEPLOY VTA ON INTEL FPGA vta/config/de10nano_config.json to vta_config.json Step 9: Go to vta/hardware/intel and run make command Step 10: Get the generated .sof file programmed into hardware Step 11: Evaluate the unit test script test_vta_insn

milestones that ensure readiness to begin development of highly complex and tightly integrated hardware and software. However, Agile inherently serves as a risk mitigation strategy, since early working applies to programs with software costs over $15M and with COTS or Government off-the-Shelf (GOTS) hardware installation or technology refresh. It does not apply to business systems or IS embedded in weapon Key Questions to Validate Systems Engineering Strategies:  Does the program leverage existing hardware platforms?33  Does the design integrate mature technologies and align with enterprise architectures

removing artificial barriers that have made the accidental tasks inordinately hard, such as severe hardware constraints, awkward programming languages, lack of machine time. How much of what software engineers desperate cries for a silver bullet – something to make software costs drop as rapidly as computer hardware costs do…. Not only are there no silver bullets now in view, the very nature of software makes breakthrough promises to give the sort of magical results with which we are so familiar in the hardware area, we must consider those attacks which address the essence of the software problem, the formulation

been integrated by: ○ MATLAB Coder ○ ONNX RuntimeArm platform support in TVM upstream IPs Target Hardware/Model Options Codegen CPU arm_cpu pixel2 (snapdragon 835), mate10/mate10pro (kirin 970), p20/p20pro runtime plugins? ○ Integrate TVM codegen into Arm NN? ● CI and benchmark testing for TVM on member hardware platforms ○ Shall we maintain a list of Arm platforms supported by TVM? More details from our

relay.vm.compile Relay Object (hardware independent) Code segment VM Func 0 VM Func 1 ... VM Func N Data segment Const 0 Const 1 ... Const K Kernel lib (hardware dependent) Packed Func 0 Packed

which can handle operations on numeric values that cannot be represented effectively in native hardware representations, but at the cost of relatively slower performance. The following are Julia's primitive floating-point numbers are also supported (Float16) on all platforms, with native instructions used on hardware which supports this number format. Otherwise, operations are implemented in software, and use Float32 to launch additional workers on the same host, thereby leveraging multi-core and multi-processor hardware. Thus, a minimal cluster manager would need to: • be a subtype of the abstract ClusterManager

which can handle operations on numeric values that cannot be represented effectively in native hardware representations, but at the cost of relatively slower performance. The following are Julia's primitive perform modular arithmetic, mirroring the char- acteristics of integer arithmetic on modern computer hardware. In scenarios where overflow is a possibility, it is crucial to explicitly check for wraparound floating-point numbers are also supported (Float16) on all platforms, with native instructions used on hardware which supports this number format. Otherwise, operations are implemented in software, and use Float32

which can handle operations on numeric values that cannot be represented effectively in native hardware representations, but at the cost of relatively slower performance. The following are Julia's primitive perform modular arithmetic, mirroring the char- acteristics of integer arithmetic on modern computer hardware. In scenarios where overflow is a possibility, it is crucial to explicitly check for wraparound floating-point numbers are also supported (Float16) on all platforms, with native instructions used on hardware which supports this number format. Otherwise, operations are implemented in software, and use Float32