ニュース
Achronix 7nm core out today
It had revenues of $100 million last year and has 120 employees. It looks like the setting for a liquidity event.
CEO Robert Blake (pictured) agrees that all the makings of a successful IPO are in place but would not be drawn on when, or even if.
That might be because he’s found a winning formula and wants to play it.
The formula can be encapsulated as “the only path left to improve energy-performance-cost is specialisation.”
And what can deliver specialisation better than a base technology which can be tailored to any application?
“It used to be easier to get gains,” says Blake, “now it takes more soul-searching to get improvements.”
Moore’s Law was the old easy route to gains, now gains come from architectures for efficient data acceleration.
More specifically, the best compute performance comes from architectures that target specific applications and data sets.
Achronix claims that its 4th generation eFPGA core, Speedcore 7t, put on the market today, delivers 300% better performance for AI and machine learning applications than the Gen 3 core.
Achronix claims the generational improvement is 60% faster performance, 50% lower power and 65% smaller die size.
The big architectural improvement is adding the Machine Learning Processor (MLP) optimised for efficient large-scale matrix-vector multiplication for AI/ML applications.
“It allows data to be moved in much bigger chunks,” simplifies Blake.
MLP blocks are flexible compute engines tightly coupled with embedded memories to give the highest performance/watt and lowest cost solution for AI/ML applications.
“Achronix was the first company to deliver production eFPGA IP to companies developing SoCs, enabling them to create programmable hardware data accelerators supporting new applications,” says Blake, “the new Speedcore Gen4 eFPGA architecture provides an optimal balance of hardware acceleration previously found only in ASIC implementations and adds the flexibility and reprogrammability of our production-proven FPGA technology to support increasing demand for new AI/ML and high data bandwidth applications.”
Achronix permits customers the ultimate in specialised computing by allowing them to define the core they want in specifying the LUTs, memory, LRAM, MLP and custom blocks.
Achronix describes the background to the development of the Speedcore architecture like this:
‘The dramatic increase in fixed and wireless network bandwidth, coupled with the redistribution of processing, and the emergence of billions of IoT devices will stress traditional network and compute infrastructure.
‘Classic Cloud and Enterprise Data Center computing resources and communications infrastructure can no longer keep pace with exponential growth in data rates, the rapidly changing security protocols, or the many new networking and connectivity requirements.
‘Traditional multicore CPUs and SoCs cannot meet these requirements unaided. They need hardware accelerators, often reprogrammable to pre-process and offload computations to increase the system’s overall compute performance.
‘Speedcore Gen4 is the Optimal AI/ML Accelerator
In addition to the general requirements of compute and networking infrastructure, AI/ML demands a significant increase in high-density, targeted computing.
‘The new Achronix MLP exploits the specific attributes of AI/ML processing and increases performance for these applications by 300% compared to previous Achronix FPGA products.
‘This is done through multiple architectural innovations that increase operating performance and the number of operations per clock cycle.
‘The MLP is a complete AI/ML compute engine. Each MLP includes a local cyclical register file that leverages temporal locality for optimal reuse of stored weights or data.
CEO Robert Blake (pictured) agrees that all the makings of a successful IPO are in place but would not be drawn on when, or even if.
That might be because he’s found a winning formula and wants to play it.
The formula can be encapsulated as “the only path left to improve energy-performance-cost is specialisation.”
And what can deliver specialisation better than a base technology which can be tailored to any application?
“It used to be easier to get gains,” says Blake, “now it takes more soul-searching to get improvements.”
Moore’s Law was the old easy route to gains, now gains come from architectures for efficient data acceleration.
More specifically, the best compute performance comes from architectures that target specific applications and data sets.
Achronix claims that its 4th generation eFPGA core, Speedcore 7t, put on the market today, delivers 300% better performance for AI and machine learning applications than the Gen 3 core.
Achronix claims the generational improvement is 60% faster performance, 50% lower power and 65% smaller die size.
The big architectural improvement is adding the Machine Learning Processor (MLP) optimised for efficient large-scale matrix-vector multiplication for AI/ML applications.
“It allows data to be moved in much bigger chunks,” simplifies Blake.
MLP blocks are flexible compute engines tightly coupled with embedded memories to give the highest performance/watt and lowest cost solution for AI/ML applications.
“Achronix was the first company to deliver production eFPGA IP to companies developing SoCs, enabling them to create programmable hardware data accelerators supporting new applications,” says Blake, “the new Speedcore Gen4 eFPGA architecture provides an optimal balance of hardware acceleration previously found only in ASIC implementations and adds the flexibility and reprogrammability of our production-proven FPGA technology to support increasing demand for new AI/ML and high data bandwidth applications.”
Achronix permits customers the ultimate in specialised computing by allowing them to define the core they want in specifying the LUTs, memory, LRAM, MLP and custom blocks.
Achronix describes the background to the development of the Speedcore architecture like this:
‘The dramatic increase in fixed and wireless network bandwidth, coupled with the redistribution of processing, and the emergence of billions of IoT devices will stress traditional network and compute infrastructure.
‘Classic Cloud and Enterprise Data Center computing resources and communications infrastructure can no longer keep pace with exponential growth in data rates, the rapidly changing security protocols, or the many new networking and connectivity requirements.
‘Traditional multicore CPUs and SoCs cannot meet these requirements unaided. They need hardware accelerators, often reprogrammable to pre-process and offload computations to increase the system’s overall compute performance.
‘Speedcore Gen4 is the Optimal AI/ML Accelerator
In addition to the general requirements of compute and networking infrastructure, AI/ML demands a significant increase in high-density, targeted computing.
‘The new Achronix MLP exploits the specific attributes of AI/ML processing and increases performance for these applications by 300% compared to previous Achronix FPGA products.
‘This is done through multiple architectural innovations that increase operating performance and the number of operations per clock cycle.
‘The MLP is a complete AI/ML compute engine. Each MLP includes a local cyclical register file that leverages temporal locality for optimal reuse of stored weights or data.