Arch & Perf Exploration

Architecture

Custom Chip Architecture Design:

Tailored architecture solutions for high-performance CPUs, ML accelerators, and GPUs.
Design and optimization of specialized instruction sets, memory hierarchies, and interconnects.

Scalable Architecture Solutions:

Designing for scalability across various processing units, from single-core to multi-core and heterogeneous systems.
Design strategies for scalable and modular systems to accommodate future chip generations.

High-Performance Processing:

Architecture optimization for high-throughput processing and low-latency operations, focusing on real-time and compute-intensive applications.
Efficient use of vector processing, SIMD/SIMT, and custom functional units to accelerate specific workloads.

Performance

Simulation and Modeling:

Use of advanced tools, methodologies to simulate and model chip performance under various workloads, helping to predict bottlenecks and optimize architecture choices.
Performance modeling of various architectural components (cores, caches, interconnects) and validation of design assumptions.

Performance Optimization:

Profiling and tuning of chip designs for peak performance across multiple workloads.
Energy-efficient architecture solutions for power-constrained applications.
Hardware/software co-optimization for maximum throughput and minimal latency.

Inference and Training Optimization:

Chip design for efficient model inference and large-scale ML model training, with optimizations for throughput and scalability.
Custom hardware for accelerating popular ML frameworks like TensorFlow, PyTorch, and others.

HW uArch & Design

Microarchitecture

Microarchitecture Development:

Detailed design of microarchitectural elements, including pipelines, control units, execution units, and cache hierarchies.
Development of custom instruction sets and micro-operations for specialized workloads and applications.

Designing efficient processing pipelines to reduce bottlenecks in data flow, leveraging parallelism and high-throughput architectures to accelerate computation in AI models.

Energy-Efficient Microarchitectures:

Designing power-efficient microarchitectures by leveraging techniques like dynamic voltage scaling, idle-state power management, and task-specific accelerators to reduce power consumption while maintaining high performance.

Design

High-Performance CPU Design:

Microarchitecture design for general-purpose and specialized CPUs, focusing on speed, parallelism, and low-latency operations.
Optimization for multi-threaded performance and single-thread efficiency.
Low-power CPU designs with dynamic voltage and frequency scaling (DVFS) and adaptive power features for energy efficiency.

System-on-Chip (SoC) Design:

Design of integrated SoC architectures, including both processing cores and peripheral components, for compact, high-performance solutions.
Implementation of interconnect fabrics (e.g., ARM AMBA, NoC) to efficiently manage communication between cores and peripherals.

Memory Architecture and Optimization:

Design of memory subsystems, including L1/L2/L3 caches, memory controllers, and custom memory hierarchies to reduce latency and enhance bandwidth.
Development of high-bandwidth memory interfaces (HBM) and interconnects to optimize data flow and prevent bottlenecks.

ML SW & Kernels

Kernels Optimization

Kernel Optimization for Chip Architecture:

Tailored kernel optimizations to fully leverage custom chip architectures, ensuring efficient resource utilization and low-latency execution.
Fine-tuning kernel scheduling, memory management, and interrupt handling to align with hardware capabilities for improved system responsiveness.

ML Compilers

Custom ML Compiler Design:

Designing and building custom ML compilers tailored to specific hardware architectures (CPUs, GPUs, TPUs, and accelerators) for optimized execution of deep learning models.
Ensuring support for a wide range of ML frameworks (e.g., TensorFlow, PyTorch, JAX) with specialized compiler backends for efficient code generation.

Operator Fusion and Kernel Specialization:

Applying advanced optimizations such as operator fusion, where multiple operations in a model are combined into a single kernel to reduce memory transfers and improve execution efficiency.
Tailoring ML operators to specific hardware capabilities to achieve high performance by generating specialized, low-level kernel implementations.

Quantization and Mixed-Precision Compilation:

Enabling support for model quantization (e.g., INT8, FP16) through the compiler to reduce model size and improve inference speed while maintaining accuracy.
Implementing mixed-precision training and inference optimizations, leveraging hardware that supports lower-precision arithmetic for faster computations.

Runtime Software

Low-Level Software and Driver Optimization:

Fine-tuning device drivers, interrupt handling, and low-level system software to ensure efficient communication between the kernel and hardware.

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