History of Computer Architecture

History of Computer Architecture

• IBM System/360 was a single instruction set architecture spanning four distinct IBM computer lines.
• Maurice Wilkes, a two-time Turing Award recipient, introduced using a two-dimensional matrix for simplified computer control.
• The 1964 IBM System/360 announcement presented a family of compatible computers, varying in memory speed and cost.
• Fred Brooks, another Turing Award winner, led the IBM System/360 project.
• The transition to semiconductors led to microcoded machines with complex instruction sets.
• Intel's 432, a pioneering 32-bit microprocessor with security and object-oriented features, faced usability and performance challenges.
• Intel's 8086, a crucial emergency project, became a key component of the PC era due to its compatibility with existing PC software.
• The rise of high-level programming languages shifted the focus from assembly language to compiler output, highlighting the significance of instruction set architecture.
• Reduced instruction set computer (RISC) architectures countered complex microcode interpreters, emphasizing simpler instructions for quicker execution.
• IBM and DEC researchers highlighted the inefficiency of complex instruction sets, proposing simpler instructions.

Rise of RISC

• Berkeley and Stanford significantly contributed to RISC architecture development, including projects like RISC I, RISC II, and MIPS.
• RISC architectures boosted performance via simpler instructions and pipelining techniques.
• Graduate students at Berkeley and Stanford created microprocessors superior to industry models in 1984.
• Intel adapted RISC principles to enhance x86 processors, translating x86 instructions into RISC instructions in hardware.

Post-PC Era and Beyond

• System-on-a-chip architectures and domain-specific computing gained prominence in the post-PC era, focusing on cost, energy efficiency, and specialized functions.
• Moore's Law's rate of transistor density increase has substantially slowed, compared to past trends.
• Dennard scaling, enabling faster transistors with reduced power, has reached its limit.
• The dramatic rise in computer performance, fueled by Moore's Law, has slowed, leading to diminishing returns.
• Quantum computing shows promise but faces a practical implementation time lag, requiring compelling use cases to drive wide adoption.
• Spectre and Meltdown highlighted significant security vulnerabilities in modern microprocessors, emphasizing better security measures.

Software-Centric and Domain-Specific Computing

• Domain-specific architectures and languages enhance performance and efficiency through specific application optimization.
• Scripting languages like Python exhibit performance limitations compared to compiled languages like C.
• Domain-specific architectures leverage specialized hardware and software for tasks and domains.
• Machine learning applications fostered innovation in domain-specific architectures, leading to hardware like Google's TPUs.
• TPUs offer superior performance compared to CPUs and GPUs via large parallel processing and specialized memory.
• MLPerf benchmarks standardize machine learning application performance evaluation.
• Machine learning training needs exponential computing power, requiring specialized hardware and architectures.
• Google developed multiple TPU generations, offering petabyte performance and cloud access for machine learning workloads.

Open Instruction Sets and RISC-V

• Open instruction sets, compared to proprietary ones, benefit from increased competition, faster innovation, and better security control.
• RISC-V, an open instruction set architecture designed at Berkeley, fosters customization and innovation.
• RISC-V gained substantial traction, attracting support from prominent companies like NVIDIA and Western Digital.
• Open instruction sets facilitate open-source implementations, increasing transparency and security.
• The RISC-V Foundation fosters RISC-V architecture promotion and development.
• Open instruction sets address security and trust concerns inherent in proprietary architectures.

Agile Hardware Development

• Agile development methodologies, initially for software, facilitate hardware development through rapid prototyping via simulation and FPGAs.
• Cloud-based FPGA services provide cost-effective and accessible hardware design evaluation.
• Small test chips with millions of transistors enable rapid hardware design prototyping and evaluation.
• Berkeley's focus on physical chip development instills confidence and expertise in hardware.
• Agile hardware development shortens iterations and feedback loops, encouraging rapid progress and innovation.

Lessons Learned and Personal Reflections

• Prioritizing personal happiness over wealth is crucial, as wealth does not guarantee happiness.
• Family is a priority, requiring time and effort for strong bonds.
• Intellectual courage is essential for upholding intellectual values despite opposition.
• High-impact projects are more fulfilling than incremental gains.
• Honest feedback and constructive criticism are vital for improvement and growth.
• Avoiding self-proclaimed superiority fosters constructive feedback and growth.
• Focusing on one significant project with smaller projects in between maintains focus and meaningful progress.
• Optimism manages research uncertainties and difficult situations.
• Long-term relationships require open communication, admitting mistakes, and expressing affection.
• Security by obscurity is a flawed strategy; open transparent systems are more secure and trustworthy.

Conclusion

• The future of computer architecture leans towards domain-specific architectures and hardware-software co-design.
• Open instruction sets like RISC-V play a key role in driving innovation and creating a secure, agile hardware development environment.
• The next decade promises significant advancements through domain-specific architectures, open hardware platforms, and agile development methods.
• Computer architecture is an exciting field with the potential for groundbreaking innovations across various domains.