Computer Architecture & AI Hardware Engineer

What the job is

An architecture engineer decides what the chip should do and how fast. You study workloads, build performance models, and choose cache sizes, pipeline depth, memory bandwidth or accelerator dataflow before RTL is written.

AI hardware engineers apply the same thinking to neural network accelerators: systolic arrays, quantization, on-chip buffers and dataflow. The goal is more operations per watt and per byte of memory traffic.

This role sits at the very start of the flow. It needs computer architecture depth, programming skill in C++ or Python, and enough RTL knowledge to know what is buildable.

Skill ladder

Three stages, in order. Each ends with something you can show, not just something you studied.

1. Foundation4-6 months

   Skills

   • Digital design and Verilog
   • Computer architecture: ISA, pipelines, hazards
   • Caches: mapping, replacement, write policies
   • C, C++ and Python
   • Performance metrics: CPI, IPC, Amdahl's law
   • Linear algebra basics for neural networks

   Tools

   Icarus Verilog or VerilatorPythongem5git

   You are here when: A 5-stage pipelined RV32I core in Verilog and a Python cache simulator that reports hit rates on traces.

2. Job-ready9-15 months more, often during an M.Tech or MS

   Skills

   • Branch prediction, out-of-order execution basics
   • Memory hierarchy and bandwidth analysis
   • Systolic arrays and dataflow (weight, output, input stationary)
   • Quantization: INT8 and its accuracy effects
   • Roofline modeling
   • Performance modeling in gem5 or a custom simulator

   Tools

   gem5ChipyardLiteXVerilatorPython with NumPy

   You are here when: A systolic array accelerator in RTL plus a performance model that predicts its throughput within a stated error.

3. Senior6-10 years on the job

   Skills

   • Defining product architecture and roadmaps
   • Workload analysis across many applications
   • Power and area modeling at system level
   • Hardware-software co-design with compilers
   • Leading cross-team architecture reviews

   Tools

   In-house performance modelsgem5PythonRTL and emulation platforms

   You are here when: Your architecture proposal shipped in a product and met its performance targets.

Learning path

1. Digital electronicsLogic and timing basics.
2. VerilogKnow what is buildable in hardware.
3. NPTEL Computer ArchitectureFree IIT course on pipelines, caches and memory.
4. Build a pipelined CPUA 5-stage RV32I with forwarding, stalls and a test program.
5. Write a cache simulatorPython or C++, with configurable size, associativity and policy.
6. Accelerator projectA systolic array or quantized MAC engine with throughput numbers.
7. Plan postgraduate studyMost architecture roles want an M.Tech, MS or PhD.

Projects that prove it

• Architecture · Intermediate · 6-8 weeksFive-Stage Pipelined RV32I CPUA classic IF-ID-EX-MEM-WB RV32I pipeline with forwarding and hazard detection, the core every architecture interview asks you to draw.
• Architecture · Intermediate · 4-6 weeksGshare and Tournament Branch PredictorCompare bimodal, gshare and tournament predictors in a trace-driven simulator, then build the winner with a BTB in RTL.
• AI hardware · Intermediate · 5-7 weeksWeight-Stationary Systolic Array for GEMMA 16 x 16 weight-stationary systolic array that multiplies tiled matrices, with measured utilization and a clean RTL-to-GDS run.

More: Architecture projects · AI hardware projects · Research projects

What interviews test

• Pipeline hazards and forwarding
• Cache design: associativity, replacement, write-back vs write-through
• Average memory access time calculation
• Branch prediction schemes
• Out-of-order execution: register renaming, reorder buffer
• Amdahl's law and performance metrics
• Systolic array dataflow and utilization
• Roofline model: compute-bound vs memory-bound
• Quantization trade-offs
• Coding: a simulator or algorithm in C++ or Python

Mistakes freshers make

• Reading papers without building anything, so implement one idea and measure it.
• Ignoring RTL, so build at least one core or accelerator yourself.
• Quoting peak TOPS only, so measure utilization and memory bandwidth too.
• Delaying the postgraduate decision, so plan GATE or MS applications from 3rd year.

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